EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "refer to this solution :https://www.kaggle.com/jsaguiar/lightgbm-7th-place-solution\n",
    "\n",
    "https://www.tinymind.cn/articles/3655\n",
    "\n",
    "https://github.com/Featuretools/Automated-Manual-Comparison/tree/master/Loan%20Repayment\n",
    "\n",
    "https://www.kaggle.com/willkoehrsen/start-here-a-gentle-introduction/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import featuretools as ft\n",
    "import lightgbm as lgb\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "RSEED = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in data and check basic info\n",
    "def read_check_data(file_path):\n",
    "    data = pd.read_csv(file_path)\n",
    "    print('Training data shape: ', data.shape)\n",
    "    print(data.head())\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (307511, 122)\n",
      "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
      "0      100002       1         Cash loans           M            N   \n",
      "1      100003       0         Cash loans           F            N   \n",
      "2      100004       0    Revolving loans           M            Y   \n",
      "3      100006       0         Cash loans           F            N   \n",
      "4      100007       0         Cash loans           M            N   \n",
      "\n",
      "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
      "0               Y             0          202500.0    406597.5      24700.5   \n",
      "1               N             0          270000.0   1293502.5      35698.5   \n",
      "2               Y             0           67500.0    135000.0       6750.0   \n",
      "3               Y             0          135000.0    312682.5      29686.5   \n",
      "4               Y             0          121500.0    513000.0      21865.5   \n",
      "\n",
      "              ...              FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
      "0             ...                             0                0   \n",
      "1             ...                             0                0   \n",
      "2             ...                             0                0   \n",
      "3             ...                             0                0   \n",
      "4             ...                             0                0   \n",
      "\n",
      "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
      "0                0                0                        0.0   \n",
      "1                0                0                        0.0   \n",
      "2                0                0                        0.0   \n",
      "3                0                0                        NaN   \n",
      "4                0                0                        0.0   \n",
      "\n",
      "  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
      "0                       0.0                         0.0   \n",
      "1                       0.0                         0.0   \n",
      "2                       0.0                         0.0   \n",
      "3                       NaN                         NaN   \n",
      "4                       0.0                         0.0   \n",
      "\n",
      "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
      "0                        0.0                        0.0   \n",
      "1                        0.0                        0.0   \n",
      "2                        0.0                        0.0   \n",
      "3                        NaN                        NaN   \n",
      "4                        0.0                        0.0   \n",
      "\n",
      "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
      "0                         1.0  \n",
      "1                         0.0  \n",
      "2                         0.0  \n",
      "3                         NaN  \n",
      "4                         0.0  \n",
      "\n",
      "[5 rows x 122 columns]\n"
     ]
    }
   ],
   "source": [
    "app_train = read_check_data(\"./data/application_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (48744, 121)\n",
      "   SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  \\\n",
      "0      100001         Cash loans           F            N               Y   \n",
      "1      100005         Cash loans           M            N               Y   \n",
      "2      100013         Cash loans           M            Y               Y   \n",
      "3      100028         Cash loans           F            N               Y   \n",
      "4      100038         Cash loans           M            Y               N   \n",
      "\n",
      "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
      "0             0          135000.0    568800.0      20560.5         450000.0   \n",
      "1             0           99000.0    222768.0      17370.0         180000.0   \n",
      "2             0          202500.0    663264.0      69777.0         630000.0   \n",
      "3             2          315000.0   1575000.0      49018.5        1575000.0   \n",
      "4             1          180000.0    625500.0      32067.0         625500.0   \n",
      "\n",
      "              ...             FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
      "0             ...                            0                0   \n",
      "1             ...                            0                0   \n",
      "2             ...                            0                0   \n",
      "3             ...                            0                0   \n",
      "4             ...                            0                0   \n",
      "\n",
      "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
      "0                0                0                        0.0   \n",
      "1                0                0                        0.0   \n",
      "2                0                0                        0.0   \n",
      "3                0                0                        0.0   \n",
      "4                0                0                        NaN   \n",
      "\n",
      "   AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
      "0                        0.0                         0.0   \n",
      "1                        0.0                         0.0   \n",
      "2                        0.0                         0.0   \n",
      "3                        0.0                         0.0   \n",
      "4                        NaN                         NaN   \n",
      "\n",
      "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
      "0                        0.0                        0.0   \n",
      "1                        0.0                        0.0   \n",
      "2                        0.0                        1.0   \n",
      "3                        0.0                        0.0   \n",
      "4                        NaN                        NaN   \n",
      "\n",
      "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
      "0                         0.0  \n",
      "1                         3.0  \n",
      "2                         4.0  \n",
      "3                         3.0  \n",
      "4                         NaN  \n",
      "\n",
      "[5 rows x 121 columns]\n"
     ]
    }
   ],
   "source": [
    "app_test = read_check_data(\"./data/application_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "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_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48739</th>\n",
       "      <td>456221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>412560.0</td>\n",
       "      <td>17473.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48740</th>\n",
       "      <td>456222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>622413.0</td>\n",
       "      <td>31909.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48741</th>\n",
       "      <td>456223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>33205.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48742</th>\n",
       "      <td>456224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>25128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48743</th>\n",
       "      <td>456250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312768.0</td>\n",
       "      <td>24709.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "48739      456221     NaN         Cash loans           F            N   \n",
       "48740      456222     NaN         Cash loans           F            N   \n",
       "48741      456223     NaN         Cash loans           F            Y   \n",
       "48742      456224     NaN         Cash loans           M            N   \n",
       "48743      456250     NaN         Cash loans           F            Y   \n",
       "\n",
       "      FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
       "48739               Y             0          121500.0    412560.0   \n",
       "48740               N             2          157500.0    622413.0   \n",
       "48741               Y             1          202500.0    315000.0   \n",
       "48742               N             0          225000.0    450000.0   \n",
       "48743               N             0          135000.0    312768.0   \n",
       "\n",
       "       AMT_ANNUITY             ...              FLAG_DOCUMENT_18  \\\n",
       "48739      17473.5             ...                             0   \n",
       "48740      31909.5             ...                             0   \n",
       "48741      33205.5             ...                             0   \n",
       "48742      25128.0             ...                             0   \n",
       "48743      24709.5             ...                             0   \n",
       "\n",
       "      FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "48739                0                0                0   \n",
       "48740                0                0                0   \n",
       "48741                0                0                0   \n",
       "48742                0                0                0   \n",
       "48743                0                0                0   \n",
       "\n",
       "      AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "48739                        0.0                       0.0   \n",
       "48740                        NaN                       NaN   \n",
       "48741                        0.0                       0.0   \n",
       "48742                        0.0                       0.0   \n",
       "48743                        0.0                       0.0   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "48739                         0.0                        0.0   \n",
       "48740                         NaN                        NaN   \n",
       "48741                         0.0                        0.0   \n",
       "48742                         0.0                        0.0   \n",
       "48743                         0.0                        0.0   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "48739                        0.0                         1.0  \n",
       "48740                        NaN                         NaN  \n",
       "48741                        3.0                         1.0  \n",
       "48742                        0.0                         2.0  \n",
       "48743                        1.0                         4.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app = app_train.append(app_test,sort=False)\n",
    "app.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (10001358, 8)\n",
      "   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",
      "   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"
     ]
    }
   ],
   "source": [
    "POS_CASH_balance = read_check_data(\"./data/POS_CASH_balance.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (27299925, 3)\n",
      "   SK_ID_BUREAU  MONTHS_BALANCE STATUS\n",
      "0       5715448               0      C\n",
      "1       5715448              -1      C\n",
      "2       5715448              -2      C\n",
      "3       5715448              -3      C\n",
      "4       5715448              -4      C\n"
     ]
    }
   ],
   "source": [
    "bureau_balance = read_check_data(\"./data/bureau_balance.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (1670214, 37)\n",
      "   SK_ID_PREV  SK_ID_CURR NAME_CONTRACT_TYPE  AMT_ANNUITY  AMT_APPLICATION  \\\n",
      "0     2030495      271877     Consumer loans     1730.430          17145.0   \n",
      "1     2802425      108129         Cash loans    25188.615         607500.0   \n",
      "2     2523466      122040         Cash loans    15060.735         112500.0   \n",
      "3     2819243      176158         Cash loans    47041.335         450000.0   \n",
      "4     1784265      202054         Cash loans    31924.395         337500.0   \n",
      "\n",
      "   AMT_CREDIT  AMT_DOWN_PAYMENT  AMT_GOODS_PRICE WEEKDAY_APPR_PROCESS_START  \\\n",
      "0     17145.0               0.0          17145.0                   SATURDAY   \n",
      "1    679671.0               NaN         607500.0                   THURSDAY   \n",
      "2    136444.5               NaN         112500.0                    TUESDAY   \n",
      "3    470790.0               NaN         450000.0                     MONDAY   \n",
      "4    404055.0               NaN         337500.0                   THURSDAY   \n",
      "\n",
      "   HOUR_APPR_PROCESS_START            ...            NAME_SELLER_INDUSTRY  \\\n",
      "0                       15            ...                    Connectivity   \n",
      "1                       11            ...                             XNA   \n",
      "2                       11            ...                             XNA   \n",
      "3                        7            ...                             XNA   \n",
      "4                        9            ...                             XNA   \n",
      "\n",
      "   CNT_PAYMENT  NAME_YIELD_GROUP       PRODUCT_COMBINATION  \\\n",
      "0         12.0            middle  POS mobile with interest   \n",
      "1         36.0        low_action          Cash X-Sell: low   \n",
      "2         12.0              high         Cash X-Sell: high   \n",
      "3         12.0            middle       Cash X-Sell: middle   \n",
      "4         24.0              high         Cash Street: high   \n",
      "\n",
      "   DAYS_FIRST_DRAWING DAYS_FIRST_DUE DAYS_LAST_DUE_1ST_VERSION  DAYS_LAST_DUE  \\\n",
      "0            365243.0          -42.0                     300.0          -42.0   \n",
      "1            365243.0         -134.0                     916.0       365243.0   \n",
      "2            365243.0         -271.0                      59.0       365243.0   \n",
      "3            365243.0         -482.0                    -152.0         -182.0   \n",
      "4                 NaN            NaN                       NaN            NaN   \n",
      "\n",
      "  DAYS_TERMINATION NFLAG_INSURED_ON_APPROVAL  \n",
      "0            -37.0                       0.0  \n",
      "1         365243.0                       1.0  \n",
      "2         365243.0                       1.0  \n",
      "3           -177.0                       1.0  \n",
      "4              NaN                       NaN  \n",
      "\n",
      "[5 rows x 37 columns]\n"
     ]
    }
   ],
   "source": [
    "previous_application = read_check_data(\"./data/previous_application.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (1670214, 37)\n",
      "   SK_ID_PREV  SK_ID_CURR NAME_CONTRACT_TYPE  AMT_ANNUITY  AMT_APPLICATION  \\\n",
      "0     2030495      271877     Consumer loans     1730.430          17145.0   \n",
      "1     2802425      108129         Cash loans    25188.615         607500.0   \n",
      "2     2523466      122040         Cash loans    15060.735         112500.0   \n",
      "3     2819243      176158         Cash loans    47041.335         450000.0   \n",
      "4     1784265      202054         Cash loans    31924.395         337500.0   \n",
      "\n",
      "   AMT_CREDIT  AMT_DOWN_PAYMENT  AMT_GOODS_PRICE WEEKDAY_APPR_PROCESS_START  \\\n",
      "0     17145.0               0.0          17145.0                   SATURDAY   \n",
      "1    679671.0               NaN         607500.0                   THURSDAY   \n",
      "2    136444.5               NaN         112500.0                    TUESDAY   \n",
      "3    470790.0               NaN         450000.0                     MONDAY   \n",
      "4    404055.0               NaN         337500.0                   THURSDAY   \n",
      "\n",
      "   HOUR_APPR_PROCESS_START            ...            NAME_SELLER_INDUSTRY  \\\n",
      "0                       15            ...                    Connectivity   \n",
      "1                       11            ...                             XNA   \n",
      "2                       11            ...                             XNA   \n",
      "3                        7            ...                             XNA   \n",
      "4                        9            ...                             XNA   \n",
      "\n",
      "   CNT_PAYMENT  NAME_YIELD_GROUP       PRODUCT_COMBINATION  \\\n",
      "0         12.0            middle  POS mobile with interest   \n",
      "1         36.0        low_action          Cash X-Sell: low   \n",
      "2         12.0              high         Cash X-Sell: high   \n",
      "3         12.0            middle       Cash X-Sell: middle   \n",
      "4         24.0              high         Cash Street: high   \n",
      "\n",
      "   DAYS_FIRST_DRAWING DAYS_FIRST_DUE DAYS_LAST_DUE_1ST_VERSION  DAYS_LAST_DUE  \\\n",
      "0            365243.0          -42.0                     300.0          -42.0   \n",
      "1            365243.0         -134.0                     916.0       365243.0   \n",
      "2            365243.0         -271.0                      59.0       365243.0   \n",
      "3            365243.0         -482.0                    -152.0         -182.0   \n",
      "4                 NaN            NaN                       NaN            NaN   \n",
      "\n",
      "  DAYS_TERMINATION NFLAG_INSURED_ON_APPROVAL  \n",
      "0            -37.0                       0.0  \n",
      "1         365243.0                       1.0  \n",
      "2         365243.0                       1.0  \n",
      "3           -177.0                       1.0  \n",
      "4              NaN                       NaN  \n",
      "\n",
      "[5 rows x 37 columns]\n"
     ]
    }
   ],
   "source": [
    "credit_card_balance = read_check_data(\"./data/previous_application.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (1716428, 17)\n",
      "   SK_ID_CURR  SK_ID_BUREAU CREDIT_ACTIVE CREDIT_CURRENCY  DAYS_CREDIT  \\\n",
      "0      215354       5714462        Closed      currency 1         -497   \n",
      "1      215354       5714463        Active      currency 1         -208   \n",
      "2      215354       5714464        Active      currency 1         -203   \n",
      "3      215354       5714465        Active      currency 1         -203   \n",
      "4      215354       5714466        Active      currency 1         -629   \n",
      "\n",
      "   CREDIT_DAY_OVERDUE  DAYS_CREDIT_ENDDATE  DAYS_ENDDATE_FACT  \\\n",
      "0                   0               -153.0             -153.0   \n",
      "1                   0               1075.0                NaN   \n",
      "2                   0                528.0                NaN   \n",
      "3                   0                  NaN                NaN   \n",
      "4                   0               1197.0                NaN   \n",
      "\n",
      "   AMT_CREDIT_MAX_OVERDUE  CNT_CREDIT_PROLONG  AMT_CREDIT_SUM  \\\n",
      "0                     NaN                   0         91323.0   \n",
      "1                     NaN                   0        225000.0   \n",
      "2                     NaN                   0        464323.5   \n",
      "3                     NaN                   0         90000.0   \n",
      "4                 77674.5                   0       2700000.0   \n",
      "\n",
      "   AMT_CREDIT_SUM_DEBT  AMT_CREDIT_SUM_LIMIT  AMT_CREDIT_SUM_OVERDUE  \\\n",
      "0                  0.0                   NaN                     0.0   \n",
      "1             171342.0                   NaN                     0.0   \n",
      "2                  NaN                   NaN                     0.0   \n",
      "3                  NaN                   NaN                     0.0   \n",
      "4                  NaN                   NaN                     0.0   \n",
      "\n",
      "       CREDIT_TYPE  DAYS_CREDIT_UPDATE  AMT_ANNUITY  \n",
      "0  Consumer credit                -131          NaN  \n",
      "1      Credit card                 -20          NaN  \n",
      "2  Consumer credit                 -16          NaN  \n",
      "3      Credit card                 -16          NaN  \n",
      "4  Consumer credit                 -21          NaN  \n"
     ]
    }
   ],
   "source": [
    "bureau = read_check_data(\"./data/bureau.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_missing_col(df):\n",
    "    missing_num = df.isnull().sum().sort_values(ascending = False)\n",
    "    minssing_percent = (df.isnull().mean()*100).sort_values(ascending = False)\n",
    "    missing_info  = pd.concat([missing_num, minssing_percent], axis=1, keys=['missing_num', 'minssing_percent'])\n",
    "    print(missing_info.head())\n",
    "    return missing_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_missing_row(df):\n",
    "    missing_num = df.isnull().sum(axis=1).sort_values(ascending = False)\n",
    "    minssing_percent = (df.isnull().mean(axis=1)*100).sort_values(ascending = False)\n",
    "    missing_info  = pd.concat([missing_num, minssing_percent], axis=1, keys=['missing_num', 'minssing_percent'])\n",
    "    print(missing_info.head())\n",
    "    return missing_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_hist(missing_info):\n",
    "    plt.hist(missing_info.minssing_percent)\n",
    "    print(missing_info.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          missing_num  minssing_percent\n",
      "COMMONAREA_MEDI                214865         69.872297\n",
      "COMMONAREA_AVG                 214865         69.872297\n",
      "COMMONAREA_MODE                214865         69.872297\n",
      "NONLIVINGAPARTMENTS_MODE       213514         69.432963\n",
      "NONLIVINGAPARTMENTS_MEDI       213514         69.432963\n",
      "         missing_num  minssing_percent\n",
      "count     122.000000        122.000000\n",
      "mean    75020.204918         24.395942\n",
      "std     87476.136655         28.446507\n",
      "min         0.000000          0.000000\n",
      "25%         0.000000          0.000000\n",
      "50%      1021.000000          0.332021\n",
      "75%    156271.000000         50.818020\n",
      "max    214865.000000         69.872297\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check missing value of app_train\n",
    "miss_app_train = check_missing_col(app_train)\n",
    "missing_hist(miss_app_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        missing_num  minssing_percent\n",
      "133770           61              50.0\n",
      "244833           61              50.0\n",
      "150206           61              50.0\n",
      "69707            61              50.0\n",
      "269786           61              50.0\n",
      "         missing_num  minssing_percent\n",
      "count  307511.000000     307511.000000\n",
      "mean       29.763049         24.395942\n",
      "std        20.919345         17.147004\n",
      "min         0.000000          0.000000\n",
      "25%         5.000000          4.098361\n",
      "50%        36.000000         29.508197\n",
      "75%        49.000000         40.163934\n",
      "max        61.000000         50.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_hist(check_missing_row(app_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       missing_num  minssing_percent\n",
      "CNT_INSTALMENT_FUTURE        26087          0.260835\n",
      "CNT_INSTALMENT               26071          0.260675\n",
      "SK_DPD_DEF                       0          0.000000\n",
      "SK_DPD                           0          0.000000\n",
      "NAME_CONTRACT_STATUS             0          0.000000\n",
      "        missing_num  minssing_percent\n",
      "count      8.000000          8.000000\n",
      "mean    6519.750000          0.065189\n",
      "std    12072.231948          0.120706\n",
      "min        0.000000          0.000000\n",
      "25%        0.000000          0.000000\n",
      "50%        0.000000          0.000000\n",
      "75%     6517.750000          0.065169\n",
      "max    26087.000000          0.260835\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check missing value of POS_CASH_balance\n",
    "miss_POS_CASH = check_missing_col(POS_CASH_balance)\n",
    "missing_hist(miss_POS_CASH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         missing_num  minssing_percent\n",
      "8095302            2              25.0\n",
      "6099819            2              25.0\n",
      "7188626            2              25.0\n",
      "7188625            2              25.0\n",
      "5799073            2              25.0\n",
      "        missing_num  minssing_percent\n",
      "count  1.000136e+07      1.000136e+07\n",
      "mean   5.215092e-03      6.518865e-02\n",
      "std    1.018921e-01      1.273651e+00\n",
      "min    0.000000e+00      0.000000e+00\n",
      "25%    0.000000e+00      0.000000e+00\n",
      "50%    0.000000e+00      0.000000e+00\n",
      "75%    0.000000e+00      0.000000e+00\n",
      "max    2.000000e+00      2.500000e+01\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_hist(check_missing_row(POS_CASH_balance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                missing_num  minssing_percent\n",
      "STATUS                    0               0.0\n",
      "MONTHS_BALANCE            0               0.0\n",
      "SK_ID_BUREAU              0               0.0\n",
      "       missing_num  minssing_percent\n",
      "count          3.0               3.0\n",
      "mean           0.0               0.0\n",
      "std            0.0               0.0\n",
      "min            0.0               0.0\n",
      "25%            0.0               0.0\n",
      "50%            0.0               0.0\n",
      "75%            0.0               0.0\n",
      "max            0.0               0.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "miss_bureau_balance = check_missing_col(bureau_balance)\n",
    "missing_hist(miss_bureau_balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          missing_num  minssing_percent\n",
      "27299924            0               0.0\n",
      "9099834             0               0.0\n",
      "9099966             0               0.0\n",
      "9099967             0               0.0\n",
      "9099968             0               0.0\n",
      "       missing_num  minssing_percent\n",
      "count   27299925.0        27299925.0\n",
      "mean           0.0               0.0\n",
      "std            0.0               0.0\n",
      "min            0.0               0.0\n",
      "25%            0.0               0.0\n",
      "50%            0.0               0.0\n",
      "75%            0.0               0.0\n",
      "max            0.0               0.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_hist(check_missing_row(bureau_balance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          missing_num  minssing_percent\n",
      "RATE_INTEREST_PRIVILEGED      1664263         99.643698\n",
      "RATE_INTEREST_PRIMARY         1664263         99.643698\n",
      "RATE_DOWN_PAYMENT              895844         53.636480\n",
      "AMT_DOWN_PAYMENT               895844         53.636480\n",
      "NAME_TYPE_SUITE                820405         49.119754\n",
      "        missing_num  minssing_percent\n",
      "count  3.700000e+01         37.000000\n",
      "mean   3.002523e+05         17.976877\n",
      "std    4.602509e+05         27.556401\n",
      "min    0.000000e+00          0.000000\n",
      "25%    0.000000e+00          0.000000\n",
      "50%    0.000000e+00          0.000000\n",
      "75%    6.730650e+05         40.298129\n",
      "max    1.664263e+06         99.643698\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check_missing(previous_application)\n",
    "missing_hist(check_missing_col(previous_application))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        missing_num  minssing_percent\n",
      "402355           15         40.540541\n",
      "545870           15         40.540541\n",
      "24543            15         40.540541\n",
      "205845           15         40.540541\n",
      "366790           15         40.540541\n",
      "        missing_num  minssing_percent\n",
      "count  1.670214e+06      1.670214e+06\n",
      "mean   6.651445e+00      1.797688e+01\n",
      "std    4.648636e+00      1.256388e+01\n",
      "min    0.000000e+00      0.000000e+00\n",
      "25%    2.000000e+00      5.405405e+00\n",
      "50%    4.000000e+00      1.081081e+01\n",
      "75%    1.100000e+01      2.972973e+01\n",
      "max    1.500000e+01      4.054054e+01\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_hist(check_missing_row(previous_application))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          missing_num  minssing_percent\n",
      "RATE_INTEREST_PRIVILEGED      1664263         99.643698\n",
      "RATE_INTEREST_PRIMARY         1664263         99.643698\n",
      "RATE_DOWN_PAYMENT              895844         53.636480\n",
      "AMT_DOWN_PAYMENT               895844         53.636480\n",
      "NAME_TYPE_SUITE                820405         49.119754\n",
      "        missing_num  minssing_percent\n",
      "count  3.700000e+01         37.000000\n",
      "mean   3.002523e+05         17.976877\n",
      "std    4.602509e+05         27.556401\n",
      "min    0.000000e+00          0.000000\n",
      "25%    0.000000e+00          0.000000\n",
      "50%    0.000000e+00          0.000000\n",
      "75%    6.730650e+05         40.298129\n",
      "max    1.664263e+06         99.643698\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check_missing(credit_card_balance)\n",
    "missing_hist(check_missing_col(credit_card_balance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        missing_num  minssing_percent\n",
      "402355           15         40.540541\n",
      "545870           15         40.540541\n",
      "24543            15         40.540541\n",
      "205845           15         40.540541\n",
      "366790           15         40.540541\n",
      "        missing_num  minssing_percent\n",
      "count  1.670214e+06      1.670214e+06\n",
      "mean   6.651445e+00      1.797688e+01\n",
      "std    4.648636e+00      1.256388e+01\n",
      "min    0.000000e+00      0.000000e+00\n",
      "25%    2.000000e+00      5.405405e+00\n",
      "50%    4.000000e+00      1.081081e+01\n",
      "75%    1.100000e+01      2.972973e+01\n",
      "max    1.500000e+01      4.054054e+01\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_hist(check_missing_row(credit_card_balance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        missing_num  minssing_percent\n",
      "AMT_ANNUITY                 1226791         71.473490\n",
      "AMT_CREDIT_MAX_OVERDUE      1124488         65.513264\n",
      "DAYS_ENDDATE_FACT            633653         36.916958\n",
      "AMT_CREDIT_SUM_LIMIT         591780         34.477415\n",
      "AMT_CREDIT_SUM_DEBT          257669         15.011932\n",
      "        missing_num  minssing_percent\n",
      "count  1.700000e+01         17.000000\n",
      "mean   2.317616e+05         13.502552\n",
      "std    4.096962e+05         23.869116\n",
      "min    0.000000e+00          0.000000\n",
      "25%    0.000000e+00          0.000000\n",
      "50%    0.000000e+00          0.000000\n",
      "75%    2.576690e+05         15.011932\n",
      "max    1.226791e+06         71.473490\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check_missing(bureau)\n",
    "missing_hist(check_missing_col(bureau))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        missing_num  minssing_percent\n",
      "793046            6         35.294118\n",
      "773821            6         35.294118\n",
      "871866            6         35.294118\n",
      "468959            6         35.294118\n",
      "701505            6         35.294118\n",
      "        missing_num  minssing_percent\n",
      "count  1.716428e+06      1.716428e+06\n",
      "mean   2.295434e+00      1.350255e+01\n",
      "std    1.126084e+00      6.624026e+00\n",
      "min    0.000000e+00      0.000000e+00\n",
      "25%    1.000000e+00      5.882353e+00\n",
      "50%    2.000000e+00      1.176471e+01\n",
      "75%    3.000000e+00      1.764706e+01\n",
      "max    6.000000e+00      3.529412e+01\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_hist(check_missing_row(bureau))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select and Re-Encode Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_feature_type(df,data_type):\n",
    "    return df.select_dtypes(include=[data_type])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_feature_type(df):\n",
    "    return df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_col_unique(df,data_type):\n",
    "    '''count the total unique value number of each colunm\n",
    "    data_type could be object,category'''\n",
    "    return df.select_dtypes(data_type).apply(pd.Series.nunique, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_col_each_unique(df,col_name):\n",
    "    '''count total number of observations of each unique value of a colunm'''\n",
    "    return df.groupby(col_name)[col_name].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_distribution(df,col_name):\n",
    "    print(df[col_name].describe())\n",
    "    print('Total missing value number: ',df[col_name].isnull().sum())\n",
    "    plt.figure(figsize=(12,5))\n",
    "    sns.distplot(df[col_name].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_encoder(df, categorical_columns=None):\n",
    "    \"\"\"Encode categorical values as integers (0,1,2,3...) with pandas.factorize. \"\"\"\n",
    "    # if categorical_colunms are not given than treat object as categorical features\n",
    "    if not categorical_columns:\n",
    "        categorical_columns = [col for col in df.columns if df[col].dtype == 'object']\n",
    "    for col in categorical_columns:\n",
    "        df[col], uniques = pd.factorize(df[col])\n",
    "    return df, categorical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encoder(df, categorical_columns=None, nan_as_category=True):\n",
    "    \"\"\"Create a new column for each categorical value in categorical columns. \"\"\"\n",
    "    original_columns = list(df.columns)\n",
    "    if not categorical_columns:\n",
    "        categorical_columns = [col for col in df.columns if df[col].dtype == 'object']\n",
    "    df = pd.get_dummies(df, columns=categorical_columns, dummy_na=nan_as_category)\n",
    "    categorical_columns = [c for c in df.columns if c not in original_columns]\n",
    "    return df, categorical_columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## check value type and correct wrong values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    66\n",
       "int64      40\n",
       "object     16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_feature_type(app)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME_CONTRACT_TYPE             2\n",
       "CODE_GENDER                    3\n",
       "FLAG_OWN_CAR                   2\n",
       "FLAG_OWN_REALTY                2\n",
       "NAME_TYPE_SUITE                7\n",
       "NAME_INCOME_TYPE               8\n",
       "NAME_EDUCATION_TYPE            5\n",
       "NAME_FAMILY_STATUS             6\n",
       "NAME_HOUSING_TYPE              6\n",
       "OCCUPATION_TYPE               18\n",
       "WEEKDAY_APPR_PROCESS_START     7\n",
       "ORGANIZATION_TYPE             58\n",
       "FONDKAPREMONT_MODE             4\n",
       "HOUSETYPE_MODE                 3\n",
       "WALLSMATERIAL_MODE             7\n",
       "EMERGENCYSTATE_MODE            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_col_unique(app_train,'object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CODE_GENDER\n",
       "F      235126\n",
       "M      121125\n",
       "XNA         4\n",
       "Name: CODE_GENDER, dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_col_each_unique(app,'CODE_GENDER')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    3.562550e+05\n",
      "mean     1.701161e+05\n",
      "std      2.235068e+05\n",
      "min      2.565000e+04\n",
      "25%      1.125000e+05\n",
      "50%      1.530000e+05\n",
      "75%      2.025000e+05\n",
      "max      1.170000e+08\n",
      "Name: AMT_INCOME_TOTAL, dtype: float64\n",
      "Total missing value number:  0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "check_distribution(app,'AMT_INCOME_TOTAL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    356255.000000\n",
      "mean      64317.231413\n",
      "std      141705.532576\n",
      "min      -17912.000000\n",
      "25%       -2781.000000\n",
      "50%       -1224.000000\n",
      "75%        -290.000000\n",
      "max      365243.000000\n",
      "Name: DAYS_EMPLOYED, dtype: float64\n",
      "Total missing value number:  0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this discussion https://www.kaggle.com/c/home-credit-default-risk/discussion/57247#332033\n",
    "# says 365243 means NA\n",
    "check_distribution(app,'DAYS_EMPLOYED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    356254.000000\n",
      "mean       -978.580852\n",
      "std         835.063902\n",
      "min       -4361.000000\n",
      "25%       -1592.000000\n",
      "50%        -771.000000\n",
      "75%        -286.000000\n",
      "max           0.000000\n",
      "Name: DAYS_LAST_PHONE_CHANGE, dtype: float64\n",
      "Total missing value number:  1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAFBCAYAAACvjsNqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xt8XXWd7//XZ++dnXtz7y1tmpSmYEFuxhZGUcRBUBmrM6gVR/k5nGF04Djn4Zk5yvg4zhxm+I3oGXno4I0jjMhPrBxm1Co35eIdekFKaSlt02vStE2b+z3Ze39+f+yVsglJs5OmO2nyfj4e+5G1vuu7vvu7WFDe/ea7vsvcHREREREROfNC090BEREREZG5QuFbRERERCRDFL5FRERERDJE4VtEREREJEMUvkVEREREMkThW0REREQkQxS+RUREREQyROFbRERERCRDFL5FRERERDIkMt0dOJPKy8u9urp6urshIiIiIrPc888/f8LdK8arN6vDd3V1NVu2bJnuboiIiIjILGdmB9Opp2knIiIiIiIZovAtIiIiIpIhCt8iIiIiIhmi8C0iIiIikiEK3yIiIiIiGaLwLSIiIiKSIQrfIiIiIiIZklb4NrNrzWyXmdWb2edGOZ5tZj8Mjm80s+qUY7cF5bvM7JqgbKmZPWNmO81sh5n9TUr9UjP7hZntCX6WBOVmZl8L2tpmZpee7sWLiIiIiGTSuOHbzMLA14F3A6uAj5jZqhHVbgLa3H0FcBdwZ3DuKmAdcD5wLfCNoL0Y8N/d/Q3AZcAtKW1+DnjK3WuBp4J9gu+vDT43A9+c1BWLiIiIiEyTdEa+VwP17r7P3QeB9cDaEXXWAvcH2w8D7zQzC8rXu/uAu+8H6oHV7n7E3f8A4O5dwE6gcpS27gfen1L+PU96Dig2s0UTvF4RERERkWmTTviuBBpS9ht5NSi/ro67x4AOoCydc4MpKpcAG4OiBe5+JGjrCDB/Av3AzG42sy1mtuX48eNpXJ6IiIiISGZE0qhjo5R5mnVOea6ZFQD/Afw3d++cgn7g7vcA9wDU1dW97riIiIiInL4HNx5Ku+4Na6rOYE/OLumMfDcCS1P2lwBNY9UxswhQBLSe6lwzyyIZvL/v7v+ZUufY8HSS4GfzBPohIiIiIjJjpRO+NwO1ZlZjZlGSD1BuGFFnA3BjsH098LS7e1C+LlgNpYbkw5Kbgvng9wI73f0rp2jrRuAnKeUfD1Y9uQzoGJ6eIiIiIiJyNhh32om7x8zsVuAJIAzc5+47zOx2YIu7byAZpB8ws3qSI97rgnN3mNlDwMskVzi5xd3jZvZW4GPAS2a2Nfiqv3f3R4EvAg+Z2U3AIeCDwfFHgfeQfGizF/jEFFy/iIiIiEjGWHKAenaqq6vzLVu2THc3RERERGYdzfl+LTN73t3rxqunN1yKiIiIiGSIwreIiIiISIYofIuIiIiIZIjCt4iIiIhIhih8i4iIiIhkiMK3iIiIiEiGKHyLiIiIiGSIwreIiIiISIYofIuIiIiIZIjCt4iIiIhIhih8i4iIiIhkiMK3iIiIiEiGKHyLiIiIiGSIwreIiIiISIYofIuIiIiIZIjCt4iIiIhIhih8i4iIiIhkiMK3iIiIiEiGKHyLiIiIiGSIwreIiIiISIYofIuIiIiIZIjCt4iIiIhIhih8i4iIiIhkSFrh28yuNbNdZlZvZp8b5Xi2mf0wOL7RzKpTjt0WlO8ys2tSyu8zs2Yz2z6irR+a2dbgc8DMtgbl1WbWl3LsW5O9aBERERGR6RAZr4KZhYGvA1cDjcBmM9vg7i+nVLsJaHP3FWa2DrgT+LCZrQLWAecDi4EnzWylu8eB7wJ3A99L/T53/3DKd/8r0JFyeK+7XzzxyxQRERERmX7pjHyvBurdfZ+7DwLrgbUj6qwF7g+2HwbeaWYWlK939wF33w/UB+3h7r8GWsf60uD8DwE/mMD1iIiIiIjMWOmE70qgIWW/MSgbtY67x0iOVpelee5YrgCOufuelLIaM3vBzH5lZlek2Y6IiIiIyIww7rQTwEYp8zTrpHPuWD7Ca0e9jwBV7t5iZm8Cfmxm57t752s6YnYzcDNAVVVVml8lIiIiInLmpTPy3QgsTdlfAjSNVcfMIkARySkl6Zz7OkEbfwr8cLgsmLrSEmw/D+wFVo48193vcfc6d6+rqKgY9+JERERERDIlnfC9Gag1sxozi5J8gHLDiDobgBuD7euBp93dg/J1wWooNUAtsCmN7/xj4BV3bxwuMLOK4OFPzGx50Na+NNoSEREREZkRxp124u4xM7sVeAIIA/e5+w4zux3Y4u4bgHuBB8ysnuSI97rg3B1m9hDwMhADbglWOsHMfgBcCZSbWSPwD+5+b/C163j9g5ZvA243sxgQBz7p7mM+sCkiIiIiMtNYcoB6dqqrq/MtW7ZMdzdEREREZp0HNx5Ku+4Na2b/c3hm9ry7141XT2+4FBERERHJEIVvEREREZEMUfgWEREREckQhW8RERERkQxR+BYRERERyRCFbxERERGRDFH4FhERERHJEIVvEREREZEMUfgWEREREckQhW8RERERkQxR+BYRERERyRCFbxERERGRDFH4FhERERHJEIVvEREREZEMUfgWEREREckQhW8RERERkQxR+BYRERERyRCFbxERERGRDFH4FhERERHJEIVvEREREZEMUfgWEREREckQhW8RERERkQxR+BYRERERyRCFbxERERGRDEkrfJvZtWa2y8zqzexzoxzPNrMfBsc3mll1yrHbgvJdZnZNSvl9ZtZsZttHtPWPZnbYzLYGn/eM15aIiIiIyNlg3PBtZmHg68C7gVXAR8xs1YhqNwFt7r4CuAu4Mzh3FbAOOB+4FvhG0B7Ad4Oy0dzl7hcHn0fTaEtEREREZMZLZ+R7NVDv7vvcfRBYD6wdUWctcH+w/TDwTjOzoHy9uw+4+36gPmgPd/810DqBvo7ZloiIiIjI2SCd8F0JNKTsNwZlo9Zx9xjQAZSlee5objWzbcHUlJIJ9AMzu9nMtpjZluPHj6fxVSIiIiIimZFO+LZRyjzNOumcO9I3gXOAi4EjwL9OoB+4+z3uXufudRUVFeN8lYiIiIhI5qQTvhuBpSn7S4CmseqYWQQoIjmlJJ1zX8Pdj7l73N0TwP/h1aklE25LRERERGQmSSd8bwZqzazGzKIkH3rcMKLOBuDGYPt64Gl396B8XbAaSg1QC2w61ZeZ2aKU3Q8Aw6uhTLgtEREREZGZJDJeBXePmdmtwBNAGLjP3XeY2e3AFnffANwLPGBm9SRHvNcF5+4ws4eAl4EYcIu7xwHM7AfAlUC5mTUC/+Du9wJfMrOLSU4pOQD81XhtiYiIiIicDSw5QD071dXV+ZYtW6a7GyIiIiKzzoMbD6Vd94Y1VWewJzODmT3v7nXj1dMbLkVEREREMkThW0REREQkQxS+RUREREQyROFbRERERCRDFL5FRERERDJE4VtEREREJEMUvkVEREREMkThW0REREQkQxS+RUREREQyROFbRERERCRDFL5FRERERDJE4VtEREREJEMUvkVEREREMkThW0REREQkQxS+RUREREQyROFbRERERCRDFL5FRERERDJE4VtEREREJEMUvkVEREREMkThW0REREQkQxS+RUREREQyROFbRERERCRDFL5FRERERDIkrfBtZtea2S4zqzezz41yPNvMfhgc32hm1SnHbgvKd5nZNSnl95lZs5ltH9HWl83sFTPbZmY/MrPioLzazPrMbGvw+dZkL1pEREREZDqMG77NLAx8HXg3sAr4iJmtGlHtJqDN3VcAdwF3BueuAtYB5wPXAt8I2gP4blA20i+AC9z9QmA3cFvKsb3ufnHw+WR6lygiIiIiMjOkM/K9Gqh3933uPgisB9aOqLMWuD/Yfhh4p5lZUL7e3QfcfT9QH7SHu/8aaB35Ze7+c3ePBbvPAUsmeE0iIiIiIjNSOuG7EmhI2W8MykatEwTnDqAszXNP5S+Ax1L2a8zsBTP7lZldMYF2RERERESmXSSNOjZKmadZJ51zR/9Ss88DMeD7QdERoMrdW8zsTcCPzex8d+8ccd7NwM0AVVVV6XyViIiIiEhGpDPy3QgsTdlfAjSNVcfMIkARySkl6Zz7OmZ2I3Ad8FF3d4Bg6kpLsP08sBdYOfJcd7/H3evcva6ioiKNyxMRERERyYx0wvdmoNbMaswsSvIByg0j6mwAbgy2rweeDkLzBmBdsBpKDVALbDrVl5nZtcBngfe5e29KecXww5pmtjxoa18a/RcRERERmRHGnXbi7jEzuxV4AggD97n7DjO7Hdji7huAe4EHzKye5Ij3uuDcHWb2EPAyySkkt7h7HMDMfgBcCZSbWSPwD+5+L3A3kA38IvnMJs8FK5u8DbjdzGJAHPiku7/ugU0RERERkZnKglkds1JdXZ1v2bJlurshIiIiMus8uPFQ2nVvWDP7n8Mzs+fdvW68enrDpYiIiIhIhih8i4iIiIhkiMK3iIiIiEiGKHyLiIiIiGSIwreIiIiISIYofIuIiIiIZIjCt4iIiIhIhih8i4iIiIhkiMK3iIiIiEiGKHyLiIiIiGSIwreIiIiISIYofIuIiIiIZIjCt4iIiIhIhih8i4iIiIhkiMK3iIiIiEiGKHyLiIiIiGSIwreIiIiISIYofIuIiIiIZIjCt4iIiIhIhih8i4iIiIhkiMK3iIiIiEiGKHyLiIiIiGSIwreIiIiISIYofIuIiIiIZEha4dvMrjWzXWZWb2afG+V4tpn9MDi+0cyqU47dFpTvMrNrUsrvM7NmM9s+oq1SM/uFme0JfpYE5WZmXwva2mZml072okVEREREpsO44dvMwsDXgXcDq4CPmNmqEdVuAtrcfQVwF3BncO4qYB1wPnAt8I2gPYDvBmUjfQ54yt1rgaeCfYLvrw0+NwPfTO8SRURERERmhnRGvlcD9e6+z90HgfXA2hF11gL3B9sPA+80MwvK17v7gLvvB+qD9nD3XwOto3xfalv3A+9PKf+eJz0HFJvZonQuUkRERERkJkgnfFcCDSn7jUHZqHXcPQZ0AGVpnjvSAnc/ErR1BJg/gX5gZjeb2RYz23L8+PFxvkpEREREplo84XztqT1s3N8y3V2ZcdIJ3zZKmadZJ51z05VWW+5+j7vXuXtdRUXFJL9KRERERCarqb2Po539PLLtCMc6+6e7OzNKOuG7EViasr8EaBqrjplFgCKSU0rSOXekY8PTSYKfzRPoh4iIiIhMs4MtPQBkhUM8/HwjQ/HENPdo5kgnfG8Gas2sxsyiJB+g3DCizgbgxmD7euBpd/egfF2wGkoNyYclN43zfalt3Qj8JKX848GqJ5cBHcPTU0RERERk5jjQ0ktpfpQPXFLJ4fY+7n66frq7NGOMG76DOdy3Ak8AO4GH3H2Hmd1uZu8Lqt0LlJlZPfAZghVK3H0H8BDwMvA4cIu7xwHM7AfAs8C5ZtZoZjcFbX0RuNrM9gBXB/sAjwL7SD60+X+Avz6tKxcRERGRKefuHGzpobosjwsqi7hkaTF3P1PPtsb26e7ajGDJAerZqa6uzrds2TLd3RARERGZdR7ceGjU8uNdA9z15G4+cEklb64upW8wznd+u4+8aJhHPn0FOVnhUc8725nZ8+5eN149veFSRERERKbM8Hzv6rJ8AHKjYb50/YXsPd7Dl5/YNZ1dmxEUvkVERERkyhxo6SUvGqa8IHqy7IraCj5++TLu/e1+XjnaOY29m34K3yIiIiIyZZLzvfNJvm/xVTe9tQaAFxvm9txvhW8RERERmRJd/UO09AyyrCzvdccqi3OJhIwDLb3T0LOZQ+FbRERERKbEwSBYD8/3ThUJh6gqzePAiZ5Md2tGUfgWERERkSlxsKWHrLCxqDhn1OPV5fnsV/gWERERETl9B1p6WVKSRyQ0esSsLsvnYEsvs3mp6/EofIuIiIjIaRsYitPU3kf1KPO9h9WU59E3FOdY50AGezazKHyLiIiIyGlraOvDgWWjzPceVl2ePDaXp54ofIuIiIjIaTvQ0oMBVaVjj3wPP4h5oEXhW0RERERk0g629LCoKOeUr49fXJxLNBya0yueKHyLiIiIyGmJJ5yG1r5TTjkBCIeMqrI8jXyLiIiIiEzWkY4+BuOJUV+uM1J1WT4HTszdF+0ofIuIiIjIaRl+a+V4I98A1cHIdyIxN5cbVPgWERERkdNysKWHkrwsinKzxq1bXZ7PQCzB0c7+DPRs5lH4FhEREZHTcritj6WnWOUkVU2w3OBcfehS4VtEREREJm0gFqe9b4gF80Z/pfxIJ9f6nqMPXSp8i4iIiMikHe9Kvq1yfmF2WvUXzcshOzJ3lxtU+BYRERGRSWs+Gb7TG/kOhYxlZXnsn6Mrnih8i4iIiMikNXcOEDajND+a9jnVZfkc1LQTEREREZGJae7qp6wgSjhkaZ9TU57PwdbeObncoMK3iIiIiExac9cA89N82HJYdXk+g7EETR19Z6hXM5fCt4iIiIhMylA8QVvPYNoPWw6rLhtebnDuzftOK3yb2bVmtsvM6s3sc6MczzazHwbHN5pZdcqx24LyXWZ2zXhtmtlvzGxr8Gkysx8H5VeaWUfKsS+czoWLiIiIyOk53jWAk/5KJ8Oqy5Nrgs/F5QYj41UwszDwdeBqoBHYbGYb3P3llGo3AW3uvsLM1gF3Ah82s1XAOuB8YDHwpJmtDM4ZtU13vyLlu/8D+EnK9/zG3a+b7MWKiIiIyNQ5uczgBKedLCjMISdrbi43mM7I92qg3t33ufsgsB5YO6LOWuD+YPth4J1mZkH5encfcPf9QH3Q3rhtmlkhcBXw48ldmoiIiIicSc1d/YQMyiew0gkklxusLstX+B5DJdCQst8YlI1ax91jQAdQdopz02nzA8BT7t6ZUna5mb1oZo+Z2flp9F1EREREzpDmrgFK87OJhCf+GGF1Wf6cnHaSzj+p0daNGbkuzFh1Jlqe6iPAD1L2/wAsc/eLgH9jjBFxM7vZzLaY2Zbjx4+PVkVEREREpkBz58CE53sPqy7Pp6G1l/gcW24wnfDdCCxN2V8CNI1Vx8wiQBHQeopzT9mmmZWRnJryyHCZu3e6e3ew/SiQZWblIzvr7ve4e52711VUVKRxeSIiIiIyUbFEgpaeyYfvmvI8huJOU/vcWm4wnfC9Gag1sxozi5J8gHLDiDobgBuD7euBp93dg/J1wWooNUAtsCmNNj8I/Mzd+4cLzGxhMI8cM1sd9L1lYpcrIiIiIlOhpXuQhMP8eZMc+Q6WG9w/x+Z9j7vaibvHzOxW4AkgDNzn7jvM7HZgi7tvAO4FHjCzepIj3uuCc3eY2UPAy0AMuMXd4wCjtZnyteuAL47oyvXAp8wsBvQB64KALyIiIiIZ1jy80knhxFY6GVZTHqz13dLD25g7sxXGDd9wcprHoyPKvpCy3U9ytHq0c+8A7kinzZRjV45Sdjdwdzr9FREREZEzq7mrHwPKCyY38l1RmE1+NDznRr71hksRERERmbDmzgFK8qNEI5OLk2bGsjm43KDCt4iIiIhM2PGuyT9sOaymPJ8DLXPrFfMK3yIiIiIyIbF4guPdpx++l5Xl0dDaSyyemKKezXwK3yIiIiIyIYeC9bkrJvmw5bBlZXnEEs6Rjv7xK88SCt8iIiIiMiF7mrsBTnvku7I4D4DDc2itb4VvEREREZmQ+qkK3yW5ABxuU/gWERERERnVnmNdFOVmkZ0VPq12FhUlp61o5FtEREREZAz1x7tPe9QbICcrTHlB9px6xbzCt4iIiIikLZFw6punJnxDcuqJRr5FREREREZxuL2P/qEE8+ed3konw5YU52rOt4iIiIjIaPY0dwGn/7DlsOGRb3efkvZmOoVvEREREUnbnmPJlU4qpip8F+cyEEtwontwStqb6RS+RURERCRtrxztYn5hNnnRyJS0t7g4udzgXHnoUuFbRERERNK2+UArl1aVTFl7lUH4nisPXSp8i4iIiEhamtr7aGzrY3VN6ZS1OddetKPwLSIiIiJp2XygFWBKw3dRbhaF2RGNfIuIiIiIpNq0v5WC7AhvWDRvStutLMmlUSPfIiIiIiKv2nyglTctKyEcsiltt7J47rxoR+FbRERERMbV1jPI7mPdUzrlZNji4lytdiIiIiIiMmx4vvebq6c+fFeW5NLRN0T3QGzK255ppmaBRhERERGZlETC6RmMEU84ZkbIIGRGOGRkR0KYTe0Uj8nafKCVaCTEhUuKprztk8sNtvVx7sLCKW9/JlH4FhERETkDHtx4iMFYgqb2Plp7B2nrGaStd5DWnkG6+mMMxBIMxhIMxhNjthENhyjOywo+UUrzoqxcUMhn3rUyg1eStGl/KxcvKSYnKzzlbZ9cbrC9V+FbRERERNLT1jPI5gOtbD7QymPbj9LU3kfCk8cMKMyJUJIfpbIkl+xImOxIiGgkRHYkRDhkuIO7k3BIuNMzEKOtd4j2vkEaWvvoG4rz+I6j/HDLIS5aUsxFS4opyY+esk83rKk67evqGYixvamTT759+Wm3NZolxXNnrW+FbxEREZHT0NYzyOM7jvLTF5t4bl8LCU+OWC8qzuFttRVUleVRnp9NcV4WkfDpPW7XPRBj++EOXmxo5+cvH+PnLx+juiyf6y5cdPI17WfCC4faiSec1TVlZ6T98oJsouEQjXPgocu0wreZXQt8FQgD33H3L444ng18D3gT0AJ82N0PBMduA24C4sCn3f2JU7VpZt8F3g50BM3/P+6+1ZITnr4KvAfoDcr/MLnLFhEREZm4BzceAmAonkiG4MZ26pu7STiU5Ud528oKaucXsqQkl6zTDNqjKciOcNnyMi5bXkZbzyDbGtv5/d4WvvHLet5x3nyuXDl/ypcBBNh0oJWQwaVVxVPeNkAoZCwqzqGpvf+MtD+TjBu+zSwMfB24GmgENpvZBnd/OaXaTUCbu68ws3XAncCHzWwVsA44H1gMPGlmw5OUTtXm37n7wyO68m6gNvisAb4Z/BQRERHJiJbuATYdaOX5g230DsYpzsvirSvKeeOSYhYX5WT04ciS/ChvP3c+b64u5afbmnhqZzOvHOni+jctYcG8nCn9rk37W1i1eB6FOVlT2m6qyuJcDrf1nrH2Z4p0Rr5XA/Xuvg/AzNYDa4HU8L0W+Mdg+2Hg7mCkei2w3t0HgP1mVh+0RxptjrQW+J67O/CcmRWb2SJ3P5LGNYiIiIhMirvzy13H+e7vD/Cr3ccJGbxh0TzW1JRxTkX+tK9Gkpcd4cNvruL8xR38ZOth7n6mnmtWLeAtK8qnpG+DsQQvHGrno2uWTUFvx1ZZnMuv9xw/o98xE6QTviuBhpT9Rl4/4nyyjrvHzKwDKAvKnxtxbmWwfao27zCzLwBPAZ8Lwvto/agEXhO+zexm4GaAqqrTf8BARERE5qaheIKfvtjEt3+1j13HulgwL5t3njefuupSinLP3AjwZF1QWUR1eT4/fuEwj24/SvdAjGvOX3ja7b50uIOBWILVNSVT0MuxVZbk0tw1wGAsQTQye19Fk86VjfZXJk+zzkTLAW4DzgPeDJQCn51AP3D3e9y9zt3rKioqRjlFREREZGy9gzHu/e1+3v6lZ/jMQy8C8JUPXcRvP3sV73zDghkZvIcVZEe4YU0Va2pK+fWeE/zspSMkJw1M3qb9yZfr1J2Bl+ukqizOxR2OdMzuhy7TGfluBJam7C8Bmsao02hmEaAIaB3n3FHLU6aRDJjZvwN/O4F+iIiIiEzKQCzO+k0N/NvT9ZzoHmB1TSn//IELeMe586d9aslEhMx430WLiYSM3+1t4fM/3s4/r72A0CQfxNx8oJXlFfmUF2RPcU9fK/VFO8vK8s/od02ndML3ZqDWzGqAwyQfoLxhRJ0NwI3As8D1wNPu7ma2AXjQzL5C8oHLWmATyVHsUdscnscdzBl/P7A95TtuDeaHrwE6NN9bRERETtcDzx5ka0M7T71yjPbeIWrK8/mzSytZVpbP0Y4BfrCpYfxGZhgz4z1vXEQkHOLBjYcYGErwpesvnPBKKImEs+VAK++9cNEZ6umrXn3Rzhwf+Q7mcN8KPEFyWcD73H2Hmd0ObHH3DcC9wAPBA5WtJMM0Qb2HSD5IGQNucfc4wGhtBl/5fTOrIBnQtwKfDMofJbnMYD3JpQY/cdpXLyIiInPaM7ua+dpTezjePUBlcS4fuKSSFRUFZ9VI91jMjHetWsClVSXc9eRuYokEX/nQxRMK4LuOddHZH+PNZ3jKCcCiolzMFL4BcPdHSYbf1LIvpGz3Ax8c49w7gDvSaTMov2qMdhy4JZ3+ioiIiJzKoZZebv/Zyzy58xjlBVE+uqaKVYvmzYrQncrM+Js/riUrYnzp8V1EQiG+fP2FaU9B+f+eOwjA6pozH76jkRDzC7Nn/Vsu9YZLERERmTP6h+J885d7+eav9hIJGZ9793nkRcNEQrN3dQ2Av75yBUMx564nd5MVNv7fD7xx3AD+yLYjfH/jIf7yihqWlORlpJ+Vxbka+RYRERGZDX6z5zh//6OXaGjt47oLF/H5976BRUW5J99aOdt9+p0rGIonuPuZerLCIW5fe/6YI/0HTvTw2f/YxiVVxfyPa8/LWB8XF+fy0uGO8SuexRS+RUREZNZJDdR9g3Ee3X6E5w+2UV4Q5aa31nBORQHPvDL7X+iSysz47+9ayVA8wbd/vY+scIj/ed0bXhfA+4fi3PLgHwiHjH/7yCVkhTP3W4HKklx+vuMYiYRPenWWmU7hW0RERGatl5s6+MmLTfQMxHj7ygquOm9+RsPkTDByZL+qNI8/OqeM+363n9/sOc5bV5SzYn7yIdMb1lRxxyM72dHUyXc+Xpex6SbDlhTnMhhPcKJ7gPnzcjL63Zmi8C0iIiKzTvdAjJ++2MRLhztYVJTDxy+rPrmU3VxnZrz3jYsoys3iN3tO8O+/P8D8wmzesqKc7EiIB547yF9eUcMfr1qQ8b4N36PG9j6FbxEREZGZzt358dbD3PWL3QzGE1y9agFvq62Y8PrWs52ZcUVtBZcvL2NbYwe/23uCH71wmB86cn+UAAAgAElEQVS9cDjj87xTVRYnR9oPt/VxadWZfZ39dFH4FhERkVmhqb2Pz//oJZ7ZdZylJbn86aVLWDBLR0+nSiQc4tJlJVxSVcy+Ez3EE85fvX35tE3NWVycvF+zecUThW8RERE5q8UTzoObDnHnY68QTzhfuG4V0UiI0Cxbs/tMMjPOqSjghjVV09qPwpws5uVEZvVa3wrfIiIictbafriDz//oJV5s7OAtK8r4lw9cSFVZ3pxZPnA2qizJo0kj3yIiIiIzR2f/EF/5+W6+9+wBSvOz+eq6i3nfRYtn3Rsq56LK4lwaWnunuxtnjMK3iIiInBUe3HiIhDtbG9p5YvtRugdirFleytVvWEjPQJwfbGqY7i7KFKgpz+M3e44TT/isfFBW4VtERETOCvuOd/Po9iM0tfdTWZzLxy5flvF1qOXMq11QyEAswaHWXmrK86e7O1NO4VtERGQKTGSO8XQ/1Ha22Xu8m3959BWe3HmMotwsPlS3hAuXFOuByllq5YJCAHYf61L4FhEREcmU+uZuvvnLvfx462Fys8K8a9UC3rKifM69oXKuqZ1fAMCeY11cc/7Cae7N1FP4FhERmYS+wTgnugdo6RmktWeA5w+2knAoL8imvCBKQXZED/9N0vbDHXzjl/U8tv0o2ZEQN15ezV+/4xx+vuPYdHdNMiA/O0JlcS67j3VPd1fOCIVvERGRUQzGErT2DHKie4Dj3QMcONHD3uPd7G1O/mzuGjjl+TlZISoKsqkuy2fN8jJK86MZ6vnZqW8wzi92HuP/bmngN3tOUJgd4ZYrV/CJt1RTVpA93d2TDKtdUMDuY13T3Y0zQuFbRETOeqeab+3utPYMcrCll2Nd/SwpyaWzP0Zn3xBd/TH6h+LEE07cnUTCiSWczr4hOvtjr2urMCfCORUFXFFbQU15HvMLcyjNj1JWEOV39S0YnAzrx7uSn9/tPcFv609w3qJ5/NE5ZSyfhXNYJysWT/D7vS38+IXDPLHjKD2DcRYV5fB315zLxy5fxrycrOnuokyTlQsK+f3eFmLxBJFZNs1I4VtERGadrv4htjV2cKClh4MtvXQPJIN0JGTkZoXJzgqTkxUiJytMVjhENGKEzAgZhMxYUpJHQXaY/OwIBcGnNP/1U0liCae5a4DmroGTI9sl+VFqgwfGADr6hti0v4VN+1vZeaST+YXZFORE5uSa1M2d/WxtaOfFxna2NrSzraGDroEYOVkhLlhcxMVLi6kuzydkxs9ePDLd3ZVpVDu/gMFgxZPlFQXT3Z0ppfAtIiKzRmNbL7/f28JLjR3E3SnJy2LF/AKWleVRXZZPRWF2xlfIKMrN4upVC7ny3Pm81NjBb+tP8Dfrt/Ld3x/gf163ikurSjLan8ka77cL/UMJOvqH6Owb4ryFhRzt7OdoRz9HOoZ/9p38bUI4ZJy3sJD3XbwYdzh3YaEeopTXeHXFk26FbxERkZkkkXC2Nbbzu/oTNLT1kR0JsXp5KZfXlFFeOHPmCmeFQ1y6rISLq4rJjoT48hO7+NNv/J73XbSY/3HtuWfNetWxRILmzgGa2vto6uinqb2PY539DMQSr6lnlnz4dFFRDlVleaxZXkpVaR6XVBVz/uIicrLCwMSWaJS5Y0XKiifXXjC7VjxR+BYRkbPWpv2t/NPPXualwx2U5Ue57sJFXFpVcjLYzUQhMz5Yt5T3vHER3/7VXr796308seMoH12zjE++fTnz5+VMdxdfp6m9j437W9h1tIu9x7sZijsA0UiIRUU5XFJVQmleFvNysyjKTf4szIkQCb1+NHvX0W52HZ2dq1jI1MnPjrCkJJfdzbPv3xWFbxEROescaunlXx7byWPbj7KoKOesfOlKfnaEz7zrXNatruIrv9jN/c8e4PsbD3LDmio+9fZzpjWEuzs7mjp5fPtRntx5jFeOJledKMnL4k3LSqguy2dxUS6lBdGz6p+5jG8m/SZi5YJC9szCFU8UvkVE5KzR2T/E15+u599/d4BwyPjM1Sv5yyuW86MXDk931yZtcXEu//uDF/Ffr1rB3U/X871nD/LgxkOse/NS3n9JJRctKSYUOvMBN5FwXmho5/HtR3h8x1EaWvsIGby5upTb3n0evYNx5hdmz7mHRGX61M4v4Ld7Tsy6FU/SCt9mdi3wVSAMfMfdvzjieDbwPeBNQAvwYXc/EBy7DbgJiAOfdvcnTtWmmX0fqAOGgE3AX7n7kJldCfwE2B987X+6++2Tu2wRETmbxOIJfrC5gbt+sZu23kH+7NIl/N0157JgBk7RmKxlZfl8+YMXcetVK/j6M/U8uOkQ9z97kIXzcrjm/AVcc8FCVleXTlkIcXcOtfby2/oT/L6+hWf3tdDaM0hW2HjLinJufccKrl618OQqLjNpRFTmhtoFhQzGExxs7eWcWfTQ5bjh28zCwNeBq4FGYLOZbXD3l1Oq3QS0ufsKM1sH3Al82MxWAeuA84HFwJNmtjI4Z6w2vw/8eVDnQeC/AN8M9n/j7tdN/nJFRORs86vdx7njkZfZfayb1TWlfOG6VVxQWTTd3TpjlpXl86XrL+Lz713F068c4/HtR/nhlgbuf/YgedEw51QUUDu/gBULClhRUcDi4lzm5STnWBfmRE6Gc3dnIJZgIJagZyDGodZe9p/oYf+JHvYd72HLwVbae4cAmBesX/7Hb5jPuQvmkRsNE0/A49uPTuc/CpnjVi549aHLORW+gdVAvbvvAzCz9cBaIDV8rwX+Mdh+GLjbkr+XWgusd/cBYL+Z1QftMVab7v7ocKNmtglYMslrExGRs9SDGw/R3NnPo9uPsPtYN6X5UT66popVi+axrbGDbY0d093F0zKRUeS3r5zP5cvL2X2siwMtPRzvGuCpV5r5zzGm2uRFwySC4O3++uPRSIiasnyWlOTxttp8zqkooLwgqukkMuMMr3iy+1g3114wzZ2ZQumE70qgIWW/EVgzVh13j5lZB1AWlD834tzKYPuUbZpZFvAx4G9Sii83sxeBJuBv3X3HyM6a2c3AzQBVVVVpXJ6IiMwkrT2DbHjxMJv2txKNhHj3BQu5fHnZrJrzOVHRSIgLKoteM+LfPxTneNcAnf1D9A8l6B+Ks3JBIV39Q4RCRk4kFLxMKExuVpilpbnUlCcflAyFTNNIZMbLi0ZYWpo7614zn074Hu2vwiP/Lj1WnbHKR/sTdGSb3wB+7e6/Cfb/ACxz924zew/wY6D2dY243wPcA1BXVzfK3/lFRGQmGojFeeDZg3z1qT30DMR4c3Up73zDAgqytTbAaHKywiwtff3a4BVjrG3e0NpHQ2vfme6WyJRaOb+QPcdm13KD6fyJ1ggsTdlfQnLkebQ6jWYWAYqA1nHOHbNNM/sHoAL4q+Eyd+9M2X7UzL5hZuXufiKNaxARkRlqMJbg4ecbufvpPTR19HPluRVctKR4Vj1MKSKTU7ugkF/vOc5QPDFr3oKazlVsBmrNrMbMoiQfoNwwos4G4MZg+3rgaXf3oHydmWWbWQ3JkepNp2rTzP4LcA3wEXc/+bosM1sYzCPHzFYHfW+ZzEWLiMj0G4oneGhzA1f96y/5+x+9xIKiHB64aTXf/cRqBW8RAZLLDQ7FnYMtPdPdlSkz7sh3MIf7VuAJkssC3ufuO8zsdmCLu28A7gUeCB6obCUZpgnqPUTy4cwYcIu7xwFGazP4ym8BB4Fng6w9vKTg9cCnzCwG9AHrgoAvIiJngeE5xoOxBH841MZv60/Q2jNIZXEuN15ezcoFBTS09mkusoictHJBIQB7jnWzYn7hNPdmaqQ1kS5YgeTREWVfSNnuBz44xrl3AHek02ZQPmqf3P1u4O50+isiIjNPR98Qz+5tYfOBVvqG4iwpyeW9b1zGeQsLtdKGiIxqxfwCzJIrnrz7jdPdm6mhp1hEROSMiSec39Wf4P8+38gj25pwh/MXz+MtK8qpKs1T6BaRU8qNhllaksfu5tmz4onCt4iITLmdRzr5zz808pOtTTR3DTAvJ8IfnVPO5cvLKAnemCgiko6VCwrYM4uWG1T4FhGR0xZPOFsb2nhyZzNP7TzG7mPdRELGO86bz59dWsk7zpvPfzw/+kthREROpXZBIb/aPXtWPFH4FhGRSTna0c/G/S38atdxntnVTFvvEJGQ8ebqUv7X+5bxJxctplSj3CJymlYuSK54cuBED7ULzv6HLhW+RURkXO5OY1sfzx9s47l9LTy3r4UDLb1A8nXm5y4o5JrzC6mdX0huNAzA49uPTmeXRWSWqA1WOdl9rFvhW0REZqeOviF2NHXwwqF2XjjUztaGNk50DwIwLyfC6poy/vyyZbT3DrGwKIeQHpwUkTPknIrkiid7mruARdPdndOm8C0iMocNxRMcau1lb3M3O4908fKRDjbtb6Wtd+hknfKCbKpK8/ijc5IrlKSG7byo/jciImdWbjRMTVk+Lza0T3dXpoT+1BQRmcUSCae1d5Cm9r7g009Tex8HWnrZd6KbQy29xBLJ95WZQU1ZPktK8lhdncOi4lyWlOQqYIvItLvqvPnc/+wBOnqHKMrLmu7unBb9iSoiMgO4O31DcboHYvQMxOkbjDMUTzAYTzAYS34GYolkWezV8p7BGD3BOd0DMbr7Y7T2DtLWM0hrzyBtvYMkRrwLODsSoqo0j5XzC7n2/IWcU1HA8op8Vi4oJD87ojdMisiM8ycXLeY7v93PEzuO8qE3L53u7pwWhW8RkTNsIBbnYEsv+453c6Cll2Od/TR3DdAc/GztHqRnMPa6kJyukEE0EiI7EiY7EiI/O0JeNMw5FQXkZ4fJz45QnBulOC+Lotws8qLh17zcZiCWYOeRLnYemT3r6IrI7HLhkiKqSvP46bYmhW8REUlyd4529vNSYwfbD3ewvamT+uZuGlp7Sc3V0UiIeTkRCnOSYXhxcS45QXhOhugQWeEQkbARCYUIh4xIyF77M5wsz46EiIRMb4oUkVnNzLjuwkV8+9f7aOkeoKwge7q7NGkK3yIikzQUT7D9cPIBxU37W3mxsYMT3QNAcjR6xfwC3rikiHMqCqgojFJRkENZQZScrPA091xE5Oxz3YWL+cYv9/LY9qP8+WXLprs7k6bwLSKSpv6hOFsb2tm0v5UNW5s42NrDUDw5pl1eEKWqNI/LlpdSWZzLoqJcopGz/01sIiIzxRsWFXJORT4/fbFJ4VtEZDbq7B/iDwfb2HwgGNlu6GAwnsAMFs7LoW5ZKdXl+VSX5VGYc3Y/fS8iMtMlp54s5mtP7+FYZz8L5uVMd5cmReFbRCTQ3NnPpgOtbN7fyuYDbbxytJOEQyRkvHFJEZ94azWrq0upW1bKIy8dme7uiojMOX9y0SK++tQeHtl2hL94a810d2dSFL5FZE5KJJx9J3rYciAZtJ/Z1UxrT/INjllho6o0j3ecO5/q8nyWluSdnEJyrHNAwVtEZJqsmF/IeQsL+dm2JoVvEZGZKp5wDrb0sKOpk5cOd7CtsZ0dhzvpGogBUJYfZeG8HC5bXkZ1WR6LinIJh7R6iIjITPQnFy3my0/sorGtlyUledPdnQlT+BaRWWMonqChtZf9J3qob+7m8e1HOdbVT3PnwMm3OEZCxsKiHFYtnkdlcS7LyvIpL4hqqT4RkbPEdRcu4stP7OKRbUf4q7efM93dmTCFbxE5q3T0DtHQ1ktjWx+Nwc9DQeA+1NpLPOVNNfNyIiyYl8NlywtYMC+bRUW5LJiXo1FtEZGz2LKyfC5cUsRPtzUpfIuITJa70zUQ43jXAMe7Bk6+AbKpvf81YburP/aa87IjIUrzo5QVZHPFinLKC7IpL4hSXphNXlR/xImIzEZ/cuFi7nh0J/tP9FBTnj/d3ZkQ/Z9JRE5LPOH0DcXpG4zTPxQ/ud0XbPenbKfW6egbek3QPt41wEAs8br286JhlpTksrQkj9XVJTR3DVCSF6UkP0ppXpScrJCmjIiIzDHvvXAR//LYTv7XT3fw7Y+9iezI2fPyMoVvkTlqKJ6gvXeItt5BWnsGaesZpLU3+bOtd4jOvqFkeH5NoE4k91MC9eAogXk8IYN5uVnML8ymojCb8oJsqsvyKcyJUJgToSA7K7mdHSE3Gn5NuD534VT+UxARkbPR4uJc/vn9b+Tvf/QSt3z/Bb7x0UvPmhebKXyLzALxhNPeO0hbbzI4jwzTrT0pITso6xwxfSNVNBIiNytMVjhENGxkhUNkRUJkhUMU5kQoy48my8IhsiJGdHg7HCIrHOxHRuyn1A+babRaREROyw1rqoglEnzhJzv4rz/4A3ffcClZ4ZkfwNMK32Z2LfBVIAx8x92/OOJ4NvA94E1AC/Bhdz8QHLsNuAmIA5929ydO1aaZ1QDrgVLgD8DH3H3wVN8hMpv0D8Vp7x2io2+I9t5B2vuG6OgdSgbpEWF6OGB39A3hPnp7WWEjPxohLztMfjRCUW4Wi4tyT+7nRcPkZwc/g/3IWfCHl4iIyMcvryYWd27/2cv8t/Vb+eq6i2f8/8PGDd9mFga+DlwNNAKbzWyDu7+cUu0moM3dV5jZOuBO4MNmtgpYB5wPLAaeNLOVwTljtXkncJe7rzezbwVtf3Os7zjdfwAiZ0I84XT3x2jvG3w1SPcN0REE5fbeYD8I1u19r5aPNu95WCRkrw3L2RHmz0s+WHgyPKeE6rxo5Kz5NZyIiMhk/MVba4gnnDse3UkkbHzlQxfP6FWt0hn5Xg3Uu/s+ADNbD6wFUsP3WuAfg+2Hgbst+TvltcB6dx8A9ptZfdAeo7VpZjuBq4Abgjr3B+1+c6zvcB9rvG/6NLT2khsEIT0MNnmJhJNwJ+5OIgFxd+IJJ5EIylLKE4nksbg77k48kQzACR+7PJZwYvEEQ3EnlkgQizuD8eTPWCIojycYGqVO70Cc7sEYPQPDnzg9wX73QIz+oVPPg87NCp+c2pEbDZObFWZpSR4rF4RPluVFI69uZ4XJyw4TDevfJxERkZH+8m3LiSWcOx9/hbUXL+aq8xZMd5fGlE74rgQaUvYbgTVj1XH3mJl1AGVB+XMjzq0Mtkdrswxod/fYKPXH+o4TaVxDxrg7V/7vX55ca9gM8qPJh8bCQWhKzU7Dm6MFquGi19Z/dcfxk1MNUv8KMvz3EU8pd/xkPSf1HH/N+T7W+SlljFH31fZf/12k9GOs7xp5HTNNyCAcMsKh5BzmaCRMdiRENBIiOxKiODeLBYU5J/ezs5KhORmkw+RkJX/mZmlah4iIyFT71JXncNnyUi6pKpnurpxSOuF7tGG2kRFprDpjlY+WPE5VP91+YGY3AzcHu91mtmuU82T6lTPD/uIkGaX7P3fp3s9tuv9z1Efnxr1flk6ldMJ3I7A0ZX8J0DRGnUYziwBFQOs4545WfgIoNrNIMPqdWn+s73gNd78HuCeN65JpZGZb3L1uuvsh00P3f+7SvZ/bdP/nLt37V6Xzu+/NQK2Z1ZhZlOQDlBtG1NkA3BhsXw88HczF3gCsM7PsYBWTWmDTWG0G5zwTtEHQ5k/G+Q4RERERkbPCuCPfwfzqW4EnSC4LeJ+77zCz24Et7r4BuBd4IHigspVkmCao9xDJhzNjwC3uHgcYrc3gKz8LrDezfwZeCNpmrO8QERERETlbmAaPZTqY2c3BFCGZg3T/5y7d+7lN93/u0r1/lcK3iIiIiEiGaL0zEREREZEMUfgWEREREckQhW85Y8zsb83Mzaw82Dcz+5qZ1ZvZNjO7NKXujWa2J/jcmFL+JjN7KTjna6bXO854ZvZPwf3damY/N7PFQbnu/xxgZl82s1eCe/wjMytOOXZbcC93mdk1KeXXBmX1Zva5lPIaM9sY/Hvxw2B1LJmhzOyDZrbDzBJmVjfimO79HDbWfZ6zPHjttj76TOWH5JrsTwAHgfKg7D3AYyRfmHQZsDEoLwX2BT9Lgu2S4Ngm4PLgnMeAd0/3tekz7r2fl7L9aeBbuv9z5wO8C4gE23cCdwbbq4AXgWygBthLcrWrcLC9HIgGdVYF5zwErAu2vwV8arqvT59T3vs3AOcCvwTqUsp17+fw51T3ea5+NPItZ8pdwP/gtW8hXQt8z5OeI/lCpUXANcAv3L3V3duAXwDXBsfmufuznvwv+HvA+zN7GTJR7t6ZspvPq/8O6P7PAe7+c0++JA3gOZIvS4Pk/V/v7gPuvh+oB1YHn3p33+fug8B6YG3wW46rgIeD8+9H939Gc/ed7j7aW6V17+e2Ue/zNPdpWil8y5Qzs/cBh939xRGHKoGGlP3GoOxU5Y2jlMsMZ2Z3mFkD8FHgC0Gx7v/c8xckf2MBE7//ZUB7SpDX/T976d7PbWPd5zkrndfLi7yOmT0JLBzl0OeBvyf5q+fXnTZKmU+iXKbZqe6/u//E3T8PfN7MbgNuBf4B3f9ZY7z7H9T5PMmXq31/+LRR6jujDwLp/s9Q6dz70U4bpUz3fu7Q/RxB4Vsmxd3/eLRyM3sjyTl9LwbPxi0B/mBmq0n+bXdpSvUlQFNQfuWI8l8G5UtGqS/TbKz7P4oHgUdIhm/d/1livPsfPDR7HfDOYMoQjH3/GaP8BMmpSZFgBFT3fwaYwH/7qXTv57ZT3f85SdNOZEq5+0vuPt/dq929muR/dJe6+1FgA/DxYNWLy4AOdz9C8sHMd5lZiZmVkBw1fyI41mVmlwVzAD8OjDWyIjOEmdWm7L4PeCXY1v2fA8zsWuCzwPvcvTfl0AZgnZllm1kNUEvygdrNQG2wukUUWAdsCEL7M8D1wfk3ovt/ttK9n9tGvc/T3KdppZFvyaRHSa54UQ/0Ap8AcPdWM/snkv+BAtzu7q3B9qeA7wK5JOeOPobMdF80s3OBBMnVbj4ZlOv+zw13k1zV4hfBb7+ec/dPuvsOM3sIeJnkdJRb3D0OYGa3kvxLWBi4z913BG19FlhvZv8MvADcm9lLkYkwsw8A/wZUAI+Y2VZ3v0b3fm5z99gp7vOcpNfLi4iIiIhkiKadiIiIiIhkiMK3iIiIiEiGKHyLiIiIiGSIwreIiIiISIYofIuIiIiIZIjCt4jMSWYWN7OtZrbDzF40s8+YWWhEna+a/f/t3XuIFWUYx/HvDy+lVBKZlWWZWkZWbhewPzLK0gpUWtJ0iaTC6g8rEiyqDdJwS7oghV3oakQ3Kk1SIbMLGUQX0bwUaoalFXQjs2j9w57+mHd0HHfP2XMWl8DfBw7svvPO+z7vDCzPzD5nRj/k7ZKGSdooqVehzxJJkyUdJWlxGusrSUsrzD1Q0rp2tnWX9Kuk+0vtYyWtKox/o6TmtIbVhfWslnRLO2PPTOtZLWmdpPGF9hmlvlsk9U0/HydpkaRNkjan49IzbbtAUkgaV9h3saQL0s8fStpQiO2N9o5L6j8lxbY+rXNGap8vaUKp71+l36dLapXUp9BWLb7uku5La8tjbC70LR7X1ZLuqBS/mVk1Tr7N7ED1T0Q0RMQwYDTZM8jvyTemhLsR2AqcD5CeTbsAaE59Lgd6RMSrwL3AuxExPCJOBepN0sYAG4Ar08uFkNQDeAoYFxHDgTOBDyOiJa2hobCehoh4tML4c1P/icBz5QuOshTDAuCtiDgJOBk4BGgpdNtGOibtuKoQ24T2Okm6DLgVGJPOy1nA9krxlTSRPS++sdReKb7ZQH/g9HRcRgI9CtuLx7UhIubUEI+Z2T6cfJvZAS8ifgZuAG7KE17gQmAd8ARZUpe7F5goqQGYA0xL7ceQJXn5mGvqDKcJeAT4Hjg3tR1K9lK039LYOyNiQ53j5/F9TfbCk75Vuo4CWiPi+bTfLmA6cJ2k3qnPl8B2SaM7ExNwJzAjIn5Mc7VGxNMd2VHSYLKLgrvZ+3y1G1+K/3rg5ohoTXPuiIiZnVqFmVkFTr7NzICI+Jbsb2K/1NQEvAIsBMamu8+kV6bPAD4CXo2ITan/Y8Czkj5I5SD9a40hlbNcBCxOczelOX8nex3zd5JekXRVtTvWHZhrBNlbSH9JTdOL5RVkd4MBhgEri/tGxJ9kFwdDCs2zyRLftrxUGPvBCmGdVp6r5MFSjEX5+VoBDJXUr7S9rfiGAN9HxI4Kc/YqlZ1MqtDXzKwqJ99mZnvkZR49ycpQ3kqJ5qdk5SAARMTbwB/A44W2d4BBwNPAKcAqSUfWOP9Y4IOU4L8JNErqlsafSpaYf0aW/D9XzwJJSTbwEDAp9rzmeG6xvAL4MbULaOtVyHu1R8QKAEkj2+hbLDu5rc64AW4rxVg0mexi6F+yMpmJxY1V4iNtuzYl2FslDUjN5bKT1zoRv5mZk28zMwBJg4BdwM/ApUAfYK2kLcB57FvK8G/67BYRv0fEyxFxNVnt8fk1htEEXJzmXAkcQVb+ko+/NiLmktWoX1Hj2Lk8yR6ZJ6RVrAfOKTZIOgwYAGwu9W2hcu13R+Y6u9adJJ0BnAS8m47dZPY9X23F9w1wvKRDASLi+ZTUbwe61RqHmVlHOPk2swNeukP9JDAv3QluAqZGxMCIGAicCIwp1Di3NcaofHtK5gaTlWZ0NIbDyJL84wvzTgOaJB2SP50jaQC+q2GJnfEe0FvSlBRnN+BhYH66Q79bRCwDDgeG1znX/cADko5Ocx3U3pNbSpqAmflxi4j+wLGSTqgUX4r/WWCepIML6+tZZ/xmZlU5+TazA1Vey7seWA4sA2alBPoSYEneMSL+Bj4GxrU5UuZs4AtJa4BPgGci4vMK/YdK2pZ/gBuB9yNiZ6HPImA82V3Y2/NH9gGzgGtqXG9d0sVII9mXTDcBG4FW4K52dmkBjiu1FWu+l1eYaylZ7fzydF5Wkn3RtJrJZLX5RQtTe7X4moGfgHWSVpHVjL/AnrKbcs23n3ZiZp2iPeV+ZmZmZma2P/nOt5mZmZlZF+nIv/PMzKwOkk4HXiw174yIEft53mZKT/sAXo+Ilrb6d7X/e3xmZvuTy07MzMzMzLqIy07MzMzMzF1tP5MAAAAqSURBVLqIk28zMzMzsy7i5NvMzMzMrIs4+TYzMzMz6yJOvs3MzMzMush/Fg9l/8/OvK8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total number of 1 missing value and add spike on value 0\n",
    "# 0 could mean nan value\n",
    "check_distribution(app,'DAYS_LAST_PHONE_CHANGE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48739</th>\n",
       "      <td>456221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>412560.0</td>\n",
       "      <td>17473.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48740</th>\n",
       "      <td>456222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>622413.0</td>\n",
       "      <td>31909.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48741</th>\n",
       "      <td>456223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>33205.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48742</th>\n",
       "      <td>456224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>25128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48743</th>\n",
       "      <td>456250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312768.0</td>\n",
       "      <td>24709.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "48739      456221     NaN         Cash loans           F            N   \n",
       "48740      456222     NaN         Cash loans           F            N   \n",
       "48741      456223     NaN         Cash loans           F            Y   \n",
       "48742      456224     NaN         Cash loans           M            N   \n",
       "48743      456250     NaN         Cash loans           F            Y   \n",
       "\n",
       "      FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
       "48739               Y             0          121500.0    412560.0   \n",
       "48740               N             2          157500.0    622413.0   \n",
       "48741               Y             1          202500.0    315000.0   \n",
       "48742               N             0          225000.0    450000.0   \n",
       "48743               N             0          135000.0    312768.0   \n",
       "\n",
       "       AMT_ANNUITY             ...              FLAG_DOCUMENT_18  \\\n",
       "48739      17473.5             ...                             0   \n",
       "48740      31909.5             ...                             0   \n",
       "48741      33205.5             ...                             0   \n",
       "48742      25128.0             ...                             0   \n",
       "48743      24709.5             ...                             0   \n",
       "\n",
       "      FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "48739                0                0                0   \n",
       "48740                0                0                0   \n",
       "48741                0                0                0   \n",
       "48742                0                0                0   \n",
       "48743                0                0                0   \n",
       "\n",
       "      AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "48739                        0.0                       0.0   \n",
       "48740                        NaN                       NaN   \n",
       "48741                        0.0                       0.0   \n",
       "48742                        0.0                       0.0   \n",
       "48743                        0.0                       0.0   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "48739                         0.0                        0.0   \n",
       "48740                         NaN                        NaN   \n",
       "48741                         0.0                        0.0   \n",
       "48742                         0.0                        0.0   \n",
       "48743                         0.0                        0.0   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "48739                        0.0                         1.0  \n",
       "48740                        NaN                         NaN  \n",
       "48741                        3.0                         1.0  \n",
       "48742                        0.0                         2.0  \n",
       "48743                        1.0                         4.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove 4 people code_gender value 'XNA'\n",
    "app = app[app['CODE_GENDER'] != 'XNA']  # 4 people with XNA code gender\n",
    "app.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "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_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48739</th>\n",
       "      <td>456221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>412560.0</td>\n",
       "      <td>17473.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48740</th>\n",
       "      <td>456222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>622413.0</td>\n",
       "      <td>31909.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48741</th>\n",
       "      <td>456223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>33205.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48742</th>\n",
       "      <td>456224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>25128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48743</th>\n",
       "      <td>456250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312768.0</td>\n",
       "      <td>24709.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "48739      456221     NaN         Cash loans           F            N   \n",
       "48740      456222     NaN         Cash loans           F            N   \n",
       "48741      456223     NaN         Cash loans           F            Y   \n",
       "48742      456224     NaN         Cash loans           M            N   \n",
       "48743      456250     NaN         Cash loans           F            Y   \n",
       "\n",
       "      FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
       "48739               Y             0          121500.0    412560.0   \n",
       "48740               N             2          157500.0    622413.0   \n",
       "48741               Y             1          202500.0    315000.0   \n",
       "48742               N             0          225000.0    450000.0   \n",
       "48743               N             0          135000.0    312768.0   \n",
       "\n",
       "       AMT_ANNUITY             ...              FLAG_DOCUMENT_18  \\\n",
       "48739      17473.5             ...                             0   \n",
       "48740      31909.5             ...                             0   \n",
       "48741      33205.5             ...                             0   \n",
       "48742      25128.0             ...                             0   \n",
       "48743      24709.5             ...                             0   \n",
       "\n",
       "      FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "48739                0                0                0   \n",
       "48740                0                0                0   \n",
       "48741                0                0                0   \n",
       "48742                0                0                0   \n",
       "48743                0                0                0   \n",
       "\n",
       "      AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "48739                        0.0                       0.0   \n",
       "48740                        NaN                       NaN   \n",
       "48741                        0.0                       0.0   \n",
       "48742                        0.0                       0.0   \n",
       "48743                        0.0                       0.0   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "48739                         0.0                        0.0   \n",
       "48740                         NaN                        NaN   \n",
       "48741                         0.0                        0.0   \n",
       "48742                         0.0                        0.0   \n",
       "48743                         0.0                        0.0   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "48739                        0.0                         1.0  \n",
       "48740                        NaN                         NaN  \n",
       "48741                        3.0                         1.0  \n",
       "48742                        0.0                         2.0  \n",
       "48743                        1.0                         4.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app['DAYS_EMPLOYED'].replace(365243, np.nan, inplace=True)\n",
    "app.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48739</th>\n",
       "      <td>456221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>412560.0</td>\n",
       "      <td>17473.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48740</th>\n",
       "      <td>456222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>622413.0</td>\n",
       "      <td>31909.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48741</th>\n",
       "      <td>456223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>33205.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48742</th>\n",
       "      <td>456224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>25128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48743</th>\n",
       "      <td>456250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312768.0</td>\n",
       "      <td>24709.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "48739      456221     NaN         Cash loans           F            N   \n",
       "48740      456222     NaN         Cash loans           F            N   \n",
       "48741      456223     NaN         Cash loans           F            Y   \n",
       "48742      456224     NaN         Cash loans           M            N   \n",
       "48743      456250     NaN         Cash loans           F            Y   \n",
       "\n",
       "      FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
       "48739               Y             0          121500.0    412560.0   \n",
       "48740               N             2          157500.0    622413.0   \n",
       "48741               Y             1          202500.0    315000.0   \n",
       "48742               N             0          225000.0    450000.0   \n",
       "48743               N             0          135000.0    312768.0   \n",
       "\n",
       "       AMT_ANNUITY             ...              FLAG_DOCUMENT_18  \\\n",
       "48739      17473.5             ...                             0   \n",
       "48740      31909.5             ...                             0   \n",
       "48741      33205.5             ...                             0   \n",
       "48742      25128.0             ...                             0   \n",
       "48743      24709.5             ...                             0   \n",
       "\n",
       "      FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "48739                0                0                0   \n",
       "48740                0                0                0   \n",
       "48741                0                0                0   \n",
       "48742                0                0                0   \n",
       "48743                0                0                0   \n",
       "\n",
       "      AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "48739                        0.0                       0.0   \n",
       "48740                        NaN                       NaN   \n",
       "48741                        0.0                       0.0   \n",
       "48742                        0.0                       0.0   \n",
       "48743                        0.0                       0.0   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "48739                         0.0                        0.0   \n",
       "48740                         NaN                        NaN   \n",
       "48741                         0.0                        0.0   \n",
       "48742                         0.0                        0.0   \n",
       "48743                         0.0                        0.0   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "48739                        0.0                         1.0  \n",
       "48740                        NaN                         NaN  \n",
       "48741                        3.0                         1.0  \n",
       "48742                        0.0                         2.0  \n",
       "48743                        1.0                         4.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app['DAYS_LAST_PHONE_CHANGE'].replace(0, np.nan, inplace=True)\n",
    "app.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change all categorical feature to numerical \n",
    "app_clean, categorical_columns = label_encoder(app, categorical_columns=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "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_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48739</th>\n",
       "      <td>456221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>412560.0</td>\n",
       "      <td>17473.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48740</th>\n",
       "      <td>456222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>622413.0</td>\n",
       "      <td>31909.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48741</th>\n",
       "      <td>456223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>33205.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48742</th>\n",
       "      <td>456224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>25128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48743</th>\n",
       "      <td>456250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312768.0</td>\n",
       "      <td>24709.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SK_ID_CURR  TARGET  NAME_CONTRACT_TYPE  CODE_GENDER  FLAG_OWN_CAR  \\\n",
       "48739      456221     NaN                   2            3             2   \n",
       "48740      456222     NaN                   2            3             2   \n",
       "48741      456223     NaN                   2            3             3   \n",
       "48742      456224     NaN                   2            4             2   \n",
       "48743      456250     NaN                   2            3             3   \n",
       "\n",
       "       FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
       "48739                2             0          121500.0    412560.0   \n",
       "48740                3             2          157500.0    622413.0   \n",
       "48741                2             1          202500.0    315000.0   \n",
       "48742                3             0          225000.0    450000.0   \n",
       "48743                3             0          135000.0    312768.0   \n",
       "\n",
       "       AMT_ANNUITY             ...              FLAG_DOCUMENT_18  \\\n",
       "48739      17473.5             ...                             0   \n",
       "48740      31909.5             ...                             0   \n",
       "48741      33205.5             ...                             0   \n",
       "48742      25128.0             ...                             0   \n",
       "48743      24709.5             ...                             0   \n",
       "\n",
       "       FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  \\\n",
       "48739                 0                 0                 0   \n",
       "48740                 0                 0                 0   \n",
       "48741                 0                 0                 0   \n",
       "48742                 0                 0                 0   \n",
       "48743                 0                 0                 0   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "48739                         0.0                        0.0   \n",
       "48740                         NaN                        NaN   \n",
       "48741                         0.0                        0.0   \n",
       "48742                         0.0                        0.0   \n",
       "48743                         0.0                        0.0   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "48739                         0.0                        0.0   \n",
       "48740                         NaN                        NaN   \n",
       "48741                         0.0                        0.0   \n",
       "48742                         0.0                        0.0   \n",
       "48743                         0.0                        0.0   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "48739                        0.0                         1.0  \n",
       "48740                        NaN                         NaN  \n",
       "48741                        3.0                         1.0  \n",
       "48742                        0.0                         2.0  \n",
       "48743                        1.0                         4.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_clean.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    8\n",
       "int64      6\n",
       "object     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check bureau data\n",
    "count_feature_type(bureau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CREDIT_ACTIVE       4\n",
       "CREDIT_CURRENCY     4\n",
       "CREDIT_TYPE        15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_col_unique(bureau,'object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1.716428e+06\n",
      "mean    -1.142108e+03\n",
      "std      7.951649e+02\n",
      "min     -2.922000e+03\n",
      "25%     -1.666000e+03\n",
      "50%     -9.870000e+02\n",
      "75%     -4.740000e+02\n",
      "max      0.000000e+00\n",
      "Name: DAYS_CREDIT, dtype: float64\n",
      "Total missing value number:  0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "check_distribution(bureau,'DAYS_CREDIT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1.716415e+06\n",
      "mean     3.549946e+05\n",
      "std      1.149811e+06\n",
      "min      0.000000e+00\n",
      "25%      5.130000e+04\n",
      "50%      1.255185e+05\n",
      "75%      3.150000e+05\n",
      "max      5.850000e+08\n",
      "Name: AMT_CREDIT_SUM, dtype: float64\n",
      "Total missing value number:  13\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "check_distribution(bureau,'AMT_CREDIT_SUM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>568800.0</td>\n",
       "      <td>20560.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>222768.0</td>\n",
       "      <td>17370.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>663264.0</td>\n",
       "      <td>69777.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>1575000.0</td>\n",
       "      <td>49018.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>625500.0</td>\n",
       "      <td>32067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET  NAME_CONTRACT_TYPE  CODE_GENDER  FLAG_OWN_CAR  \\\n",
       "0      100001     NaN                   2            3             2   \n",
       "1      100005     NaN                   2            4             2   \n",
       "2      100013     NaN                   2            4             3   \n",
       "3      100028     NaN                   2            3             2   \n",
       "4      100038     NaN                   2            4             3   \n",
       "\n",
       "   FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0                2             0          135000.0    568800.0      20560.5   \n",
       "1                2             0           99000.0    222768.0      17370.0   \n",
       "2                2             0          202500.0    663264.0      69777.0   \n",
       "3                2             2          315000.0   1575000.0      49018.5   \n",
       "4                3             1          180000.0    625500.0      32067.0   \n",
       "\n",
       "              ...              FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  \\\n",
       "0             ...                             0                 0   \n",
       "1             ...                             0                 0   \n",
       "2             ...                             0                 0   \n",
       "3             ...                             0                 0   \n",
       "4             ...                             0                 0   \n",
       "\n",
       "   FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                 0                 0                         0.0   \n",
       "1                 0                 0                         0.0   \n",
       "2                 0                 0                         0.0   \n",
       "3                 0                 0                         0.0   \n",
       "4                 0                 0                         NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                        0.0                         0.0   \n",
       "1                        0.0                         0.0   \n",
       "2                        0.0                         0.0   \n",
       "3                        0.0                         0.0   \n",
       "4                        NaN                         NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        1.0   \n",
       "3                        0.0                        0.0   \n",
       "4                        NaN                        NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                         0.0  \n",
       "1                         3.0  \n",
       "2                         4.0  \n",
       "3                         3.0  \n",
       "4                         NaN  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_base_train = app_clean[app_clean['TARGET'].notnull()]\n",
    "app_base_test = app_clean[app_clean['TARGET'].isnull()]\n",
    "app_base_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_validate(train):\n",
    "    \"\"\"Compute cross validation ROC AUC of a gradient boosting model for a given training dataset\"\"\"\n",
    "    \n",
    "    # Extract the labels\n",
    "    train_labels = np.array(train['TARGET'].astype(np.int32)).reshape((-1, ))\n",
    "    train = train.drop(columns = ['TARGET', 'SK_ID_CURR'])\n",
    "\n",
    "    # Create a  lgb training set\n",
    "    train_set = lgb.Dataset(train, label = train_labels)\n",
    "\n",
    "    # Find default hyperparameters\n",
    "    model = lgb.LGBMClassifier()\n",
    "    params = model.get_params()\n",
    "\n",
    "    # Number of estimators will be selected through early stopping\n",
    "    del params['n_estimators'], params['silent']\n",
    "\n",
    "    # Early stoppping with 5 fold cross validation\n",
    "    cv_results = lgb.cv(params, train_set, num_boost_round = 10000, metrics = 'auc', \n",
    "                        early_stopping_rounds = 100, seed = RSEED, nfold = 5)\n",
    "\n",
    "    print('Cross Validation ROC AUC: {:.5f} with std: {:.5f}.'.format(cv_results['auc-mean'][-1],\n",
    "                                                                               cv_results['auc-stdv'][-1]))\n",
    "\n",
    "    print('Number of estimators trained: {}'.format(len(cv_results['auc-mean'])))\n",
    "    \n",
    "    return cv_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation ROC AUC: 0.75575 with std: 0.00582.\n",
      "Number of estimators trained: 172\n"
     ]
    }
   ],
   "source": [
    "cv_results_baseline = cross_validate(app_base_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_submission(cv_results, train, test):\n",
    "    \"\"\"Make a submission dataframe for the Kaggle competition for a given dataset.\"\"\"\n",
    "    \n",
    "    # Extract the labels\n",
    "    train_labels = np.array(train['TARGET'].astype(np.int32)).reshape((-1, ))\n",
    "    train = train.drop(columns = ['TARGET', 'SK_ID_CURR'])\n",
    "    test_ids = list(test['SK_ID_CURR'])\n",
    "    test = test.drop(columns = ['TARGET','SK_ID_CURR'])\n",
    "    \n",
    "    # Make model with optimal number of estimators and train on training data\n",
    "    model = lgb.LGBMClassifier(n_estimators = len(cv_results['auc-mean']), random_state=RSEED)\n",
    "    model.fit(train, train_labels)\n",
    "    \n",
    "    # Make predictions on the testing data\n",
    "    preds = model.predict_proba(test)[:, 1]\n",
    "    submission = pd.DataFrame({'SK_ID_CURR': test_ids, \n",
    "                                'TARGET': preds})\n",
    "    \n",
    "    return submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_baseline = make_submission(cv_results_baseline, app_base_train, app_base_test)\n",
    "submission_baseline.to_csv('./data/submission_baseline.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Semi-Auto Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agg_numeric(df_child, parent_var, df_col_name):\n",
    "    \"\"\"\n",
    "    Groups and aggregates the numeric values in a child dataframe\n",
    "    by the parent variable.\n",
    "    \n",
    "    Parameters\n",
    "    --------\n",
    "        df_child (dataframe): \n",
    "            the child dataframe to calculate the statistics on\n",
    "        parent_var (string): \n",
    "            the parent variable used for grouping and aggregating\n",
    "        df_col_name (string): \n",
    "            the variable used to rename the columns\n",
    "        \n",
    "    Return\n",
    "    --------\n",
    "        agg (dataframe): \n",
    "            a dataframe with the statistics aggregated by the `parent_var` for \n",
    "            all numeric columns. The aggregate function are 'count', 'mean', 'max', 'min', 'sum'\n",
    "            Each observation of the parent variable will have \n",
    "            one row in the dataframe with the parent variable as the index. \n",
    "            The columns are also renamed using the `df_col_name`. Columns with all duplicate\n",
    "            values are removed. \n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # Remove id variables other than grouping variable\n",
    "    # e.g. SK_ID_BUREAU\n",
    "    for col in df_child:\n",
    "        if col != parent_var and 'SK_ID' in col:\n",
    "            df_child = df_child.drop(columns = col)\n",
    "            \n",
    "    # Only want the numeric variables\n",
    "    parent_ids = df_child[parent_var].copy()\n",
    "    numeric_df = df_child.select_dtypes('number').copy()\n",
    "    numeric_df[parent_var] = parent_ids\n",
    "\n",
    "    # Group by the specified variable and calculate the statistics\n",
    "    agg = numeric_df.groupby(parent_var).agg(['count', 'mean', 'max', 'min', 'sum'])\n",
    "\n",
    "    # Need to create new column names\n",
    "    columns = []\n",
    "\n",
    "    # Iterate through the variables names\n",
    "    for var in agg.columns.levels[0]:\n",
    "        if var != parent_var:\n",
    "            # Iterate through the stat names\n",
    "            for stat in agg.columns.levels[1]:\n",
    "                # Make a new column name for the variable and stat\n",
    "                columns.append('%s_%s_%s' % (df_col_name, var, stat))\n",
    "    \n",
    "    agg.columns = columns\n",
    "    \n",
    "    # Remove the columns with all redundant values\n",
    "    _, idx = np.unique(agg, axis = 1, return_index=True)\n",
    "    agg = agg.iloc[:, idx]\n",
    "    \n",
    "    return agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>BUREAU_DAYS_CREDIT_sum</th>\n",
       "      <th>BUREAU_DAYS_ENDDATE_FACT_sum</th>\n",
       "      <th>BUREAU_DAYS_CREDIT_min</th>\n",
       "      <th>BUREAU_DAYS_CREDIT_ENDDATE_min</th>\n",
       "      <th>BUREAU_DAYS_ENDDATE_FACT_min</th>\n",
       "      <th>BUREAU_DAYS_ENDDATE_FACT_mean</th>\n",
       "      <th>BUREAU_DAYS_CREDIT_mean</th>\n",
       "      <th>BUREAU_DAYS_CREDIT_UPDATE_sum</th>\n",
       "      <th>BUREAU_DAYS_ENDDATE_FACT_max</th>\n",
       "      <th>BUREAU_DAYS_CREDIT_UPDATE_min</th>\n",
       "      <th>...</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_DEBT_mean</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_min</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_mean</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_DEBT_max</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_max</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_DEBT_sum</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_sum</th>\n",
       "      <th>BUREAU_AMT_CREDIT_MAX_OVERDUE_min</th>\n",
       "      <th>BUREAU_AMT_CREDIT_MAX_OVERDUE_mean</th>\n",
       "      <th>BUREAU_AMT_CREDIT_MAX_OVERDUE_max</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>-5145</td>\n",
       "      <td>-3302.0</td>\n",
       "      <td>-1572</td>\n",
       "      <td>-1329.0</td>\n",
       "      <td>-1328.0</td>\n",
       "      <td>-825.500000</td>\n",
       "      <td>-735.000000</td>\n",
       "      <td>-652</td>\n",
       "      <td>-544.0</td>\n",
       "      <td>-155</td>\n",
       "      <td>...</td>\n",
       "      <td>85240.928571</td>\n",
       "      <td>85500.0</td>\n",
       "      <td>207623.571429</td>\n",
       "      <td>373239.0</td>\n",
       "      <td>378000.0</td>\n",
       "      <td>596686.5</td>\n",
       "      <td>1453365.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>-6992</td>\n",
       "      <td>-4185.0</td>\n",
       "      <td>-1437</td>\n",
       "      <td>-1072.0</td>\n",
       "      <td>-1185.0</td>\n",
       "      <td>-697.500000</td>\n",
       "      <td>-874.000000</td>\n",
       "      <td>-3999</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>-1185</td>\n",
       "      <td>...</td>\n",
       "      <td>49156.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108131.945625</td>\n",
       "      <td>245781.0</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>245781.0</td>\n",
       "      <td>865055.565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1681.029</td>\n",
       "      <td>5043.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>-5603</td>\n",
       "      <td>-3292.0</td>\n",
       "      <td>-2586</td>\n",
       "      <td>-2434.0</td>\n",
       "      <td>-2131.0</td>\n",
       "      <td>-1097.333333</td>\n",
       "      <td>-1400.750000</td>\n",
       "      <td>-3264</td>\n",
       "      <td>-540.0</td>\n",
       "      <td>-2131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22248.0</td>\n",
       "      <td>254350.125000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>810000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017400.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>-1734</td>\n",
       "      <td>-1065.0</td>\n",
       "      <td>-1326</td>\n",
       "      <td>-595.0</td>\n",
       "      <td>-683.0</td>\n",
       "      <td>-532.500000</td>\n",
       "      <td>-867.000000</td>\n",
       "      <td>-1064</td>\n",
       "      <td>-382.0</td>\n",
       "      <td>-682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94500.0</td>\n",
       "      <td>94518.900000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94537.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>189037.800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>-572</td>\n",
       "      <td>-123.0</td>\n",
       "      <td>-373</td>\n",
       "      <td>-128.0</td>\n",
       "      <td>-123.0</td>\n",
       "      <td>-123.000000</td>\n",
       "      <td>-190.666667</td>\n",
       "      <td>-163</td>\n",
       "      <td>-123.0</td>\n",
       "      <td>-121</td>\n",
       "      <td>...</td>\n",
       "      <td>189469.500000</td>\n",
       "      <td>29826.0</td>\n",
       "      <td>219042.000000</td>\n",
       "      <td>543087.0</td>\n",
       "      <td>568800.0</td>\n",
       "      <td>568408.5</td>\n",
       "      <td>657126.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            BUREAU_DAYS_CREDIT_sum  BUREAU_DAYS_ENDDATE_FACT_sum  \\\n",
       "SK_ID_CURR                                                         \n",
       "100001                       -5145                       -3302.0   \n",
       "100002                       -6992                       -4185.0   \n",
       "100003                       -5603                       -3292.0   \n",
       "100004                       -1734                       -1065.0   \n",
       "100005                        -572                        -123.0   \n",
       "\n",
       "            BUREAU_DAYS_CREDIT_min  BUREAU_DAYS_CREDIT_ENDDATE_min  \\\n",
       "SK_ID_CURR                                                           \n",
       "100001                       -1572                         -1329.0   \n",
       "100002                       -1437                         -1072.0   \n",
       "100003                       -2586                         -2434.0   \n",
       "100004                       -1326                          -595.0   \n",
       "100005                        -373                          -128.0   \n",
       "\n",
       "            BUREAU_DAYS_ENDDATE_FACT_min  BUREAU_DAYS_ENDDATE_FACT_mean  \\\n",
       "SK_ID_CURR                                                                \n",
       "100001                           -1328.0                    -825.500000   \n",
       "100002                           -1185.0                    -697.500000   \n",
       "100003                           -2131.0                   -1097.333333   \n",
       "100004                            -683.0                    -532.500000   \n",
       "100005                            -123.0                    -123.000000   \n",
       "\n",
       "            BUREAU_DAYS_CREDIT_mean  BUREAU_DAYS_CREDIT_UPDATE_sum  \\\n",
       "SK_ID_CURR                                                           \n",
       "100001                  -735.000000                           -652   \n",
       "100002                  -874.000000                          -3999   \n",
       "100003                 -1400.750000                          -3264   \n",
       "100004                  -867.000000                          -1064   \n",
       "100005                  -190.666667                           -163   \n",
       "\n",
       "            BUREAU_DAYS_ENDDATE_FACT_max  BUREAU_DAYS_CREDIT_UPDATE_min  \\\n",
       "SK_ID_CURR                                                                \n",
       "100001                            -544.0                           -155   \n",
       "100002                             -36.0                          -1185   \n",
       "100003                            -540.0                          -2131   \n",
       "100004                            -382.0                           -682   \n",
       "100005                            -123.0                           -121   \n",
       "\n",
       "                          ...                  \\\n",
       "SK_ID_CURR                ...                   \n",
       "100001                    ...                   \n",
       "100002                    ...                   \n",
       "100003                    ...                   \n",
       "100004                    ...                   \n",
       "100005                    ...                   \n",
       "\n",
       "            BUREAU_AMT_CREDIT_SUM_DEBT_mean  BUREAU_AMT_CREDIT_SUM_min  \\\n",
       "SK_ID_CURR                                                               \n",
       "100001                         85240.928571                    85500.0   \n",
       "100002                         49156.200000                        0.0   \n",
       "100003                             0.000000                    22248.0   \n",
       "100004                             0.000000                    94500.0   \n",
       "100005                        189469.500000                    29826.0   \n",
       "\n",
       "            BUREAU_AMT_CREDIT_SUM_mean  BUREAU_AMT_CREDIT_SUM_DEBT_max  \\\n",
       "SK_ID_CURR                                                               \n",
       "100001                   207623.571429                        373239.0   \n",
       "100002                   108131.945625                        245781.0   \n",
       "100003                   254350.125000                             0.0   \n",
       "100004                    94518.900000                             0.0   \n",
       "100005                   219042.000000                        543087.0   \n",
       "\n",
       "            BUREAU_AMT_CREDIT_SUM_max  BUREAU_AMT_CREDIT_SUM_DEBT_sum  \\\n",
       "SK_ID_CURR                                                              \n",
       "100001                       378000.0                        596686.5   \n",
       "100002                       450000.0                        245781.0   \n",
       "100003                       810000.0                             0.0   \n",
       "100004                        94537.8                             0.0   \n",
       "100005                       568800.0                        568408.5   \n",
       "\n",
       "            BUREAU_AMT_CREDIT_SUM_sum  BUREAU_AMT_CREDIT_MAX_OVERDUE_min  \\\n",
       "SK_ID_CURR                                                                 \n",
       "100001                    1453365.000                                NaN   \n",
       "100002                     865055.565                                0.0   \n",
       "100003                    1017400.500                                0.0   \n",
       "100004                     189037.800                                0.0   \n",
       "100005                     657126.000                                0.0   \n",
       "\n",
       "            BUREAU_AMT_CREDIT_MAX_OVERDUE_mean  \\\n",
       "SK_ID_CURR                                       \n",
       "100001                                     NaN   \n",
       "100002                                1681.029   \n",
       "100003                                   0.000   \n",
       "100004                                   0.000   \n",
       "100005                                   0.000   \n",
       "\n",
       "            BUREAU_AMT_CREDIT_MAX_OVERDUE_max  \n",
       "SK_ID_CURR                                     \n",
       "100001                                    NaN  \n",
       "100002                               5043.645  \n",
       "100003                                  0.000  \n",
       "100004                                  0.000  \n",
       "100005                                  0.000  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg = agg_numeric(bureau, 'SK_ID_CURR', 'BUREAU')\n",
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "app_clean_second = pd.merge(app_clean,bureau_agg,on='SK_ID_CURR',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "app_base_train_second = app_clean_second[app_clean_second['TARGET'].notnull()]\n",
    "app_base_test_second = app_clean_second[app_clean_second['TARGET'].isnull()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_DEBT_mean</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_min</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_mean</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_DEBT_max</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_max</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_DEBT_sum</th>\n",
       "      <th>BUREAU_AMT_CREDIT_SUM_sum</th>\n",
       "      <th>BUREAU_AMT_CREDIT_MAX_OVERDUE_min</th>\n",
       "      <th>BUREAU_AMT_CREDIT_MAX_OVERDUE_mean</th>\n",
       "      <th>BUREAU_AMT_CREDIT_MAX_OVERDUE_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>307511</th>\n",
       "      <td>100001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>568800.0</td>\n",
       "      <td>20560.5</td>\n",
       "      <td>...</td>\n",
       "      <td>85240.928571</td>\n",
       "      <td>85500.00</td>\n",
       "      <td>207623.571429</td>\n",
       "      <td>373239.0</td>\n",
       "      <td>378000.0</td>\n",
       "      <td>596686.5</td>\n",
       "      <td>1453365.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307512</th>\n",
       "      <td>100005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>222768.0</td>\n",
       "      <td>17370.0</td>\n",
       "      <td>...</td>\n",
       "      <td>189469.500000</td>\n",
       "      <td>29826.00</td>\n",
       "      <td>219042.000000</td>\n",
       "      <td>543087.0</td>\n",
       "      <td>568800.0</td>\n",
       "      <td>568408.5</td>\n",
       "      <td>657126.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307513</th>\n",
       "      <td>100013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>663264.0</td>\n",
       "      <td>69777.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26490.06</td>\n",
       "      <td>518070.015000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1262250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2072280.06</td>\n",
       "      <td>19305.0</td>\n",
       "      <td>19305.0</td>\n",
       "      <td>19305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307514</th>\n",
       "      <td>100028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>1575000.0</td>\n",
       "      <td>49018.5</td>\n",
       "      <td>...</td>\n",
       "      <td>18630.450000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>126739.590000</td>\n",
       "      <td>151015.5</td>\n",
       "      <td>393750.0</td>\n",
       "      <td>186304.5</td>\n",
       "      <td>1520875.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307515</th>\n",
       "      <td>100038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>625500.0</td>\n",
       "      <td>32067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SK_ID_CURR  TARGET  NAME_CONTRACT_TYPE  CODE_GENDER  FLAG_OWN_CAR  \\\n",
       "307511      100001     NaN                   2            3             2   \n",
       "307512      100005     NaN                   2            4             2   \n",
       "307513      100013     NaN                   2            4             3   \n",
       "307514      100028     NaN                   2            3             2   \n",
       "307515      100038     NaN                   2            4             3   \n",
       "\n",
       "        FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
       "307511                2             0          135000.0    568800.0   \n",
       "307512                2             0           99000.0    222768.0   \n",
       "307513                2             0          202500.0    663264.0   \n",
       "307514                2             2          315000.0   1575000.0   \n",
       "307515                3             1          180000.0    625500.0   \n",
       "\n",
       "        AMT_ANNUITY                ...                  \\\n",
       "307511      20560.5                ...                   \n",
       "307512      17370.0                ...                   \n",
       "307513      69777.0                ...                   \n",
       "307514      49018.5                ...                   \n",
       "307515      32067.0                ...                   \n",
       "\n",
       "        BUREAU_AMT_CREDIT_SUM_DEBT_mean  BUREAU_AMT_CREDIT_SUM_min  \\\n",
       "307511                     85240.928571                   85500.00   \n",
       "307512                    189469.500000                   29826.00   \n",
       "307513                         0.000000                   26490.06   \n",
       "307514                     18630.450000                       0.00   \n",
       "307515                              NaN                        NaN   \n",
       "\n",
       "        BUREAU_AMT_CREDIT_SUM_mean  BUREAU_AMT_CREDIT_SUM_DEBT_max  \\\n",
       "307511               207623.571429                        373239.0   \n",
       "307512               219042.000000                        543087.0   \n",
       "307513               518070.015000                             0.0   \n",
       "307514               126739.590000                        151015.5   \n",
       "307515                         NaN                             NaN   \n",
       "\n",
       "        BUREAU_AMT_CREDIT_SUM_max  BUREAU_AMT_CREDIT_SUM_DEBT_sum  \\\n",
       "307511                   378000.0                        596686.5   \n",
       "307512                   568800.0                        568408.5   \n",
       "307513                  1262250.0                             0.0   \n",
       "307514                   393750.0                        186304.5   \n",
       "307515                        NaN                             NaN   \n",
       "\n",
       "        BUREAU_AMT_CREDIT_SUM_sum  BUREAU_AMT_CREDIT_MAX_OVERDUE_min  \\\n",
       "307511                 1453365.00                                NaN   \n",
       "307512                  657126.00                                0.0   \n",
       "307513                 2072280.06                            19305.0   \n",
       "307514                 1520875.08                                0.0   \n",
       "307515                        NaN                                NaN   \n",
       "\n",
       "        BUREAU_AMT_CREDIT_MAX_OVERDUE_mean  BUREAU_AMT_CREDIT_MAX_OVERDUE_max  \n",
       "307511                                 NaN                                NaN  \n",
       "307512                                 0.0                                0.0  \n",
       "307513                             19305.0                            19305.0  \n",
       "307514                                 0.0                                0.0  \n",
       "307515                                 NaN                                NaN  \n",
       "\n",
       "[5 rows x 178 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_base_train_second.head()\n",
    "app_base_test_second.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation ROC AUC: 0.76083 with std: 0.00462.\n",
      "Number of estimators trained: 199\n"
     ]
    }
   ],
   "source": [
    "cv_results_second = cross_validate(app_base_train_second)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_second = make_submission(cv_results_second, app_base_train_second, app_base_test_second)\n",
    "submission_second.to_csv('./data/submission_second.csv', index = False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}