ALL 7 table combination code + LightGBM on that.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "# import os\n",
    "# print(os.listdir(\"../input\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Code for all 7 tables combination goes below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold,train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from imblearn.over_sampling import SMOTE\n",
    "# import fancyimpute as fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "8823755c52e81286cf9dc2704b5a5ae3d0cbc97d"
   },
   "outputs": [],
   "source": [
    "def one_hot_encoder(df, nan_as_category = True):\n",
    "    original_columns = list(df.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",
    "    new_columns = [c for c in df.columns if c not in original_columns]\n",
    "    return df, new_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "42494dbb62d0dc005b7027d6cb95ac2e27c3abb7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 122)\n",
      "(307511, 127)\n"
     ]
    }
   ],
   "source": [
    "app_train = pd.read_csv('application_train.csv')\n",
    "print(app_train.shape)\n",
    "app_train['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True)\n",
    "    # Some simple new features (percentages)\n",
    "app_train['DAYS_EMPLOYED_PERC'] = app_train['DAYS_EMPLOYED'] / app_train['DAYS_BIRTH']\n",
    "app_train['INCOME_CREDIT_PERC'] = app_train['AMT_INCOME_TOTAL'] / app_train['AMT_CREDIT']\n",
    "app_train['INCOME_PER_PERSON'] = app_train['AMT_INCOME_TOTAL'] / app_train['CNT_FAM_MEMBERS']\n",
    "app_train['ANNUITY_INCOME_PERC'] = app_train['AMT_ANNUITY'] / app_train['AMT_INCOME_TOTAL']\n",
    "app_train['PAYMENT_RATE'] = app_train['AMT_ANNUITY'] / app_train['AMT_CREDIT']\n",
    "\n",
    "print(app_train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "e97139ed9c05c79aa237740af0955d6ce204374a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9263239"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "247495d5af8ecb25b0d457b7d8a33f6756d8d035"
   },
   "outputs": [],
   "source": [
    "# app_train=fi.MICE().complete(app_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "c29219785d623bb9aee40814e77870d19b08c2b2"
   },
   "outputs": [],
   "source": [
    "# from fancyimpute import BiScaler, KNN, NuclearNormMinimization, SoftImpute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "68c529bdf3e9ee6d5ff6e3923efb2dfceafb759c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train['CODE_GENDER'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "e2e7d01128295620eb9c9ed07995452a42d4593c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F      202448\n",
       "M      105059\n",
       "XNA         4\n",
       "Name: CODE_GENDER, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train['CODE_GENDER'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "8fca16bd7c3d366b3388867d0ba49f41c3fd6838"
   },
   "outputs": [],
   "source": [
    "app_train=app_train[app_train['CODE_GENDER']!='XNA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "05e76508cdc40a1301373d675206379f857591a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    202448\n",
       "M    105059\n",
       "Name: CODE_GENDER, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train['CODE_GENDER'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "2c1d59229f5f120978bcfffdd1dc397bda21be3f"
   },
   "outputs": [],
   "source": [
    "# Categorical features with Binary encode (0 or 1; two categories)\n",
    "for bin_feature in ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']:\n",
    "        app_train[bin_feature], uniques = pd.factorize(app_train[bin_feature])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "e02aa650d3b47e8380d162000b43ef692230b178"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    1\n",
       "4    0\n",
       "Name: CODE_GENDER, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train['CODE_GENDER'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "da2a784ada0c01b393940b6c31ba331728e86115"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507, 127)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "7240422b6ea1f03679bc198964db296e20731291"
   },
   "outputs": [],
   "source": [
    "app_train,cat_cols=one_hot_encoder(app_train,nan_as_category=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "387c2299f03b3cc4ca7d3f21c8bf5341e8062ae2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507, 260)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "75759e6303b28bf9319ffd1230ddd38b09ef7222"
   },
   "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>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>AMT_GOODS_PRICE</th>\n",
       "      <th>...</th>\n",
       "      <th>WALLSMATERIAL_MODE_Mixed</th>\n",
       "      <th>WALLSMATERIAL_MODE_Monolithic</th>\n",
       "      <th>WALLSMATERIAL_MODE_Others</th>\n",
       "      <th>WALLSMATERIAL_MODE_Panel</th>\n",
       "      <th>WALLSMATERIAL_MODE_Stone, brick</th>\n",
       "      <th>WALLSMATERIAL_MODE_Wooden</th>\n",
       "      <th>WALLSMATERIAL_MODE_nan</th>\n",
       "      <th>EMERGENCYSTATE_MODE_No</th>\n",
       "      <th>EMERGENCYSTATE_MODE_Yes</th>\n",
       "      <th>EMERGENCYSTATE_MODE_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>351000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>1129500.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>297000.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 260 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET  CODE_GENDER  FLAG_OWN_CAR  FLAG_OWN_REALTY  \\\n",
       "0      100002       1            0             0                0   \n",
       "1      100003       0            1             0                1   \n",
       "2      100004       0            0             1                0   \n",
       "3      100006       0            1             0                0   \n",
       "4      100007       0            0             0                0   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0             0          202500.0    406597.5      24700.5         351000.0   \n",
       "1             0          270000.0   1293502.5      35698.5        1129500.0   \n",
       "2             0           67500.0    135000.0       6750.0         135000.0   \n",
       "3             0          135000.0    312682.5      29686.5         297000.0   \n",
       "4             0          121500.0    513000.0      21865.5         513000.0   \n",
       "\n",
       "            ...             WALLSMATERIAL_MODE_Mixed  \\\n",
       "0           ...                                    0   \n",
       "1           ...                                    0   \n",
       "2           ...                                    0   \n",
       "3           ...                                    0   \n",
       "4           ...                                    0   \n",
       "\n",
       "   WALLSMATERIAL_MODE_Monolithic  WALLSMATERIAL_MODE_Others  \\\n",
       "0                              0                          0   \n",
       "1                              0                          0   \n",
       "2                              0                          0   \n",
       "3                              0                          0   \n",
       "4                              0                          0   \n",
       "\n",
       "   WALLSMATERIAL_MODE_Panel  WALLSMATERIAL_MODE_Stone, brick  \\\n",
       "0                         0                                1   \n",
       "1                         0                                0   \n",
       "2                         0                                0   \n",
       "3                         0                                0   \n",
       "4                         0                                0   \n",
       "\n",
       "   WALLSMATERIAL_MODE_Wooden  WALLSMATERIAL_MODE_nan  EMERGENCYSTATE_MODE_No  \\\n",
       "0                          0                       0                       1   \n",
       "1                          0                       0                       1   \n",
       "2                          0                       1                       0   \n",
       "3                          0                       1                       0   \n",
       "4                          0                       1                       0   \n",
       "\n",
       "   EMERGENCYSTATE_MODE_Yes  EMERGENCYSTATE_MODE_nan  \n",
       "0                        0                        0  \n",
       "1                        0                        0  \n",
       "2                        0                        1  \n",
       "3                        0                        1  \n",
       "4                        0                        1  \n",
       "\n",
       "[5 rows x 260 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "d9ef7d2cd7e71aab78ec3fefa9a46c5fb6fca8a1"
   },
   "outputs": [],
   "source": [
    "# # NaN values for DAYS_EMPLOYED: 365.243 -> nan\n",
    "# app_train['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True)\n",
    "# # Some simple new features (percentages)\n",
    "# app_train['DAYS_EMPLOYED_PERC'] = app_train['DAYS_EMPLOYED'] / app_train['DAYS_BIRTH']\n",
    "# app_train['INCOME_CREDIT_PERC'] = app_train['AMT_INCOME_TOTAL'] / app_train['AMT_CREDIT']\n",
    "# app_train['INCOME_PER_PERSON'] = app_train['AMT_INCOME_TOTAL'] / app_train['CNT_FAM_MEMBERS']\n",
    "# app_train['ANNUITY_INCOME_PERC'] = app_train['AMT_ANNUITY'] / app_train['AMT_INCOME_TOTAL']\n",
    "# app_train['PAYMENT_RATE'] = app_train['AMT_ANNUITY'] / app_train['AMT_CREDIT']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "2138d22adf9a935e5ffe9471e67419025992a4d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507, 260)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bureau.csv and bureau_balance.csv file merging into one dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "c34e58b7a74cb94b96fbc9b31b5b3961f56aef66"
   },
   "outputs": [],
   "source": [
    "bureau = pd.read_csv('bureau.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "6007c0bfe70be6d64aae78e943814bc3e93581a0"
   },
   "outputs": [],
   "source": [
    "bb = pd.read_csv('bureau_balance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "542890b766c7ba4af2cb0e0bdcef6912e2c35046"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1716428, 17)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "37edcf924de7ad711d71532a7c9242284d605200"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27299925, 3)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "80e2fc6bb504ed916f50adba7a39b4dfa1608b15"
   },
   "outputs": [],
   "source": [
    "bb, bb_cat = one_hot_encoder(bb, nan_as_category=True)\n",
    "bureau, bureau_cat = one_hot_encoder(bureau, nan_as_category=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "59663bbfc89197d461ce25bb944aed0fd652289f"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "7f9ed17b4d810df21653510efe242864096d158b"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>SK_ID_BUREAU</th>\n",
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       "      <th>CREDIT_TYPE_Microloan</th>\n",
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       "      <th>CREDIT_TYPE_Mortgage</th>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  SK_ID_BUREAU  DAYS_CREDIT  CREDIT_DAY_OVERDUE  \\\n",
       "0      215354       5714462         -497                   0   \n",
       "1      215354       5714463         -208                   0   \n",
       "2      215354       5714464         -203                   0   \n",
       "3      215354       5714465         -203                   0   \n",
       "4      215354       5714466         -629                   0   \n",
       "\n",
       "   DAYS_CREDIT_ENDDATE  DAYS_ENDDATE_FACT  AMT_CREDIT_MAX_OVERDUE  \\\n",
       "0               -153.0             -153.0                     NaN   \n",
       "1               1075.0                NaN                     NaN   \n",
       "2                528.0                NaN                     NaN   \n",
       "3                  NaN                NaN                     NaN   \n",
       "4               1197.0                NaN                 77674.5   \n",
       "\n",
       "   CNT_CREDIT_PROLONG  AMT_CREDIT_SUM  AMT_CREDIT_SUM_DEBT       ...         \\\n",
       "0                   0         91323.0                  0.0       ...          \n",
       "1                   0        225000.0             171342.0       ...          \n",
       "2                   0        464323.5                  NaN       ...          \n",
       "3                   0         90000.0                  NaN       ...          \n",
       "4                   0       2700000.0                  NaN       ...          \n",
       "\n",
       "   CREDIT_TYPE_Loan for business development  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "3                                          0   \n",
       "4                                          0   \n",
       "\n",
       "   CREDIT_TYPE_Loan for purchase of shares (margin lending)  \\\n",
       "0                                                  0          \n",
       "1                                                  0          \n",
       "2                                                  0          \n",
       "3                                                  0          \n",
       "4                                                  0          \n",
       "\n",
       "   CREDIT_TYPE_Loan for the purchase of equipment  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "\n",
       "   CREDIT_TYPE_Loan for working capital replenishment  CREDIT_TYPE_Microloan  \\\n",
       "0                                                  0                       0   \n",
       "1                                                  0                       0   \n",
       "2                                                  0                       0   \n",
       "3                                                  0                       0   \n",
       "4                                                  0                       0   \n",
       "\n",
       "   CREDIT_TYPE_Mobile operator loan  CREDIT_TYPE_Mortgage  \\\n",
       "0                                 0                     0   \n",
       "1                                 0                     0   \n",
       "2                                 0                     0   \n",
       "3                                 0                     0   \n",
       "4                                 0                     0   \n",
       "\n",
       "   CREDIT_TYPE_Real estate loan  CREDIT_TYPE_Unknown type of loan  \\\n",
       "0                             0                                 0   \n",
       "1                             0                                 0   \n",
       "2                             0                                 0   \n",
       "3                             0                                 0   \n",
       "4                             0                                 0   \n",
       "\n",
       "   CREDIT_TYPE_nan  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "1f69a1b7578b29fe965e1dadb93748865fc7c32d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27299925, 11)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "bb.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "775d0edae33bcaf427d882f8221a0b74c9099b15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1716428, 40)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "fccebdf873eeb37986bdc6558ea423b3a3575b03"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   SK_ID_BUREAU  MONTHS_BALANCE  STATUS_0  STATUS_1  STATUS_2  STATUS_3  \\\n",
       "0       5715448               0         0         0         0         0   \n",
       "1       5715448              -1         0         0         0         0   \n",
       "2       5715448              -2         0         0         0         0   \n",
       "3       5715448              -3         0         0         0         0   \n",
       "4       5715448              -4         0         0         0         0   \n",
       "\n",
       "   STATUS_4  STATUS_5  STATUS_C  STATUS_X  STATUS_nan  \n",
       "0         0         0         1         0           0  \n",
       "1         0         0         1         0           0  \n",
       "2         0         0         1         0           0  \n",
       "3         0         0         1         0           0  \n",
       "4         0         0         1         0           0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "bb.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "9ad7ea180b350745f91ecd93e9f574571f4598cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MONTHS_BALANCE': ['mean'],\n",
       " 'SK_ID_BUREAU': ['mean'],\n",
       " 'STATUS_0': ['mean'],\n",
       " 'STATUS_1': ['mean'],\n",
       " 'STATUS_2': ['mean'],\n",
       " 'STATUS_3': ['mean'],\n",
       " 'STATUS_4': ['mean'],\n",
       " 'STATUS_5': ['mean'],\n",
       " 'STATUS_C': ['mean'],\n",
       " 'STATUS_X': ['mean'],\n",
       " 'STATUS_nan': ['mean']}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bureau balance: Perform aggregations and merge with bureau.csv\n",
    "bb_aggregations = {'MONTHS_BALANCE': ['min', 'max', 'size']}\n",
    "for col in bb.columns:\n",
    "    bb_aggregations[col] = ['mean']\n",
    "bb_aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "0ecf9449f005c24eb902db3e33314d1cb1d62af5"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "15572cce10e402eaef073fbd12bb3f26a67773cc"
   },
   "outputs": [],
   "source": [
    "bb_agg=bb.groupby('SK_ID_BUREAU').agg(bb_aggregations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "9a67ed3206ac1240caad4268c219baa20f768f13"
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_BUREAU  MONTHS_BALANCE  STATUS_0  STATUS_1  STATUS_2  STATUS_3  \\\n",
       "0       5715448               0         0         0         0         0   \n",
       "1       5715448              -1         0         0         0         0   \n",
       "2       5715448              -2         0         0         0         0   \n",
       "3       5715448              -3         0         0         0         0   \n",
       "4       5715448              -4         0         0         0         0   \n",
       "\n",
       "   STATUS_4  STATUS_5  STATUS_C  STATUS_X  STATUS_nan  \n",
       "0         0         0         1         0           0  \n",
       "1         0         0         1         0           0  \n",
       "2         0         0         1         0           0  \n",
       "3         0         0         1         0           0  \n",
       "4         0         0         1         0           0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "cb40c73fbeea382257f2b6d67ab7f7dd35361926"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>SK_ID_BUREAU</th>\n",
       "      <th>STATUS_0</th>\n",
       "      <th>STATUS_1</th>\n",
       "      <th>STATUS_2</th>\n",
       "      <th>STATUS_3</th>\n",
       "      <th>STATUS_4</th>\n",
       "      <th>STATUS_5</th>\n",
       "      <th>STATUS_C</th>\n",
       "      <th>STATUS_X</th>\n",
       "      <th>STATUS_nan</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_BUREAU</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>5001709</th>\n",
       "      <td>-48.0</td>\n",
       "      <td>5001709</td>\n",
       "      <td>0.000000</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>0.886598</td>\n",
       "      <td>0.113402</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5001710</th>\n",
       "      <td>-41.0</td>\n",
       "      <td>5001710</td>\n",
       "      <td>0.060241</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>0.578313</td>\n",
       "      <td>0.361446</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5001711</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>5001711</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.250000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5001712</th>\n",
       "      <td>-9.0</td>\n",
       "      <td>5001712</td>\n",
       "      <td>0.526316</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>0.473684</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5001713</th>\n",
       "      <td>-10.5</td>\n",
       "      <td>5001713</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MONTHS_BALANCE SK_ID_BUREAU  STATUS_0 STATUS_1 STATUS_2 STATUS_3  \\\n",
       "                       mean         mean      mean     mean     mean     mean   \n",
       "SK_ID_BUREAU                                                                    \n",
       "5001709               -48.0      5001709  0.000000      0.0      0.0      0.0   \n",
       "5001710               -41.0      5001710  0.060241      0.0      0.0      0.0   \n",
       "5001711                -1.5      5001711  0.750000      0.0      0.0      0.0   \n",
       "5001712                -9.0      5001712  0.526316      0.0      0.0      0.0   \n",
       "5001713               -10.5      5001713  0.000000      0.0      0.0      0.0   \n",
       "\n",
       "             STATUS_4 STATUS_5  STATUS_C  STATUS_X STATUS_nan  \n",
       "                 mean     mean      mean      mean       mean  \n",
       "SK_ID_BUREAU                                                   \n",
       "5001709           0.0      0.0  0.886598  0.113402          0  \n",
       "5001710           0.0      0.0  0.578313  0.361446          0  \n",
       "5001711           0.0      0.0  0.000000  0.250000          0  \n",
       "5001712           0.0      0.0  0.473684  0.000000          0  \n",
       "5001713           0.0      0.0  0.000000  1.000000          0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "7f1c73c7757dcbe65ac357db3c659a15172cd03e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_BUREAU      5.001709e+06\n",
       "MONTHS_BALANCE   -4.800000e+01\n",
       "STATUS_0          0.000000e+00\n",
       "STATUS_1          0.000000e+00\n",
       "STATUS_2          0.000000e+00\n",
       "STATUS_3          0.000000e+00\n",
       "STATUS_4          0.000000e+00\n",
       "STATUS_5          0.000000e+00\n",
       "STATUS_C          8.865979e-01\n",
       "STATUS_X          1.134021e-01\n",
       "STATUS_nan        0.000000e+00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb[bb['SK_ID_BUREAU']==5001709].sum()/(bb['SK_ID_BUREAU']==5001709).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "1bc282d1a8e57797c9ebf46ce6ce3041dd8e1937"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(817395, 11)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "f07237d2ebfa6759adfbb8e29ea7b66fea474857"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1716428, 40)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "d9ba874d962818f07db33a8adb799f4530c432c8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['MONTHS_BALANCE', 'SK_ID_BUREAU', 'STATUS_0', 'STATUS_1', 'STATUS_2', 'STATUS_3', 'STATUS_4', 'STATUS_5', 'STATUS_C', 'STATUS_X', 'STATUS_nan'], ['mean']],\n",
       "           labels=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb_agg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "8261b6e93bf09fd0c65da856ed01d6fb122d72cf"
   },
   "outputs": [],
   "source": [
    "bb_agg = bb.groupby('SK_ID_BUREAU').agg(bb_aggregations)\n",
    "bb_agg.columns = pd.Index([e[0] + \"_\" + e[1].upper() for e in bb_agg.columns.tolist()])\n",
    "bureau = bureau.join(bb_agg, how='left', on='SK_ID_BUREAU')\n",
    "bureau.drop(['SK_ID_BUREAU'], axis=1, inplace= True)\n",
    "del bb,bb_agg\n",
    "# just to save jupyter memory we will delete them\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "cdec4a8c8e07692f5c90d44ed14beb7bffddfb8e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1716428, 50)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_uuid": "e775fdb658be3539b80944a3127e2102285385de"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>DAYS_CREDIT</th>\n",
       "      <th>CREDIT_DAY_OVERDUE</th>\n",
       "      <th>DAYS_CREDIT_ENDDATE</th>\n",
       "      <th>DAYS_ENDDATE_FACT</th>\n",
       "      <th>AMT_CREDIT_MAX_OVERDUE</th>\n",
       "      <th>CNT_CREDIT_PROLONG</th>\n",
       "      <th>AMT_CREDIT_SUM</th>\n",
       "      <th>AMT_CREDIT_SUM_DEBT</th>\n",
       "      <th>AMT_CREDIT_SUM_LIMIT</th>\n",
       "      <th>...</th>\n",
       "      <th>SK_ID_BUREAU_MEAN</th>\n",
       "      <th>STATUS_0_MEAN</th>\n",
       "      <th>STATUS_1_MEAN</th>\n",
       "      <th>STATUS_2_MEAN</th>\n",
       "      <th>STATUS_3_MEAN</th>\n",
       "      <th>STATUS_4_MEAN</th>\n",
       "      <th>STATUS_5_MEAN</th>\n",
       "      <th>STATUS_C_MEAN</th>\n",
       "      <th>STATUS_X_MEAN</th>\n",
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       "      <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",
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       "      <th>1</th>\n",
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       "      <td>225000.0</td>\n",
       "      <td>171342.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <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",
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       "      <td>528.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>464323.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>215354</td>\n",
       "      <td>-203</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
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       "      <th>4</th>\n",
       "      <td>215354</td>\n",
       "      <td>-629</td>\n",
       "      <td>0</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77674.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2700000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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 × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  DAYS_CREDIT  CREDIT_DAY_OVERDUE  DAYS_CREDIT_ENDDATE  \\\n",
       "0      215354         -497                   0               -153.0   \n",
       "1      215354         -208                   0               1075.0   \n",
       "2      215354         -203                   0                528.0   \n",
       "3      215354         -203                   0                  NaN   \n",
       "4      215354         -629                   0               1197.0   \n",
       "\n",
       "   DAYS_ENDDATE_FACT  AMT_CREDIT_MAX_OVERDUE  CNT_CREDIT_PROLONG  \\\n",
       "0             -153.0                     NaN                   0   \n",
       "1                NaN                     NaN                   0   \n",
       "2                NaN                     NaN                   0   \n",
       "3                NaN                     NaN                   0   \n",
       "4                NaN                 77674.5                   0   \n",
       "\n",
       "   AMT_CREDIT_SUM  AMT_CREDIT_SUM_DEBT  AMT_CREDIT_SUM_LIMIT       ...         \\\n",
       "0         91323.0                  0.0                   NaN       ...          \n",
       "1        225000.0             171342.0                   NaN       ...          \n",
       "2        464323.5                  NaN                   NaN       ...          \n",
       "3         90000.0                  NaN                   NaN       ...          \n",
       "4       2700000.0                  NaN                   NaN       ...          \n",
       "\n",
       "   SK_ID_BUREAU_MEAN  STATUS_0_MEAN  STATUS_1_MEAN  STATUS_2_MEAN  \\\n",
       "0                NaN            NaN            NaN            NaN   \n",
       "1                NaN            NaN            NaN            NaN   \n",
       "2                NaN            NaN            NaN            NaN   \n",
       "3                NaN            NaN            NaN            NaN   \n",
       "4                NaN            NaN            NaN            NaN   \n",
       "\n",
       "   STATUS_3_MEAN  STATUS_4_MEAN  STATUS_5_MEAN  STATUS_C_MEAN  STATUS_X_MEAN  \\\n",
       "0            NaN            NaN            NaN            NaN            NaN   \n",
       "1            NaN            NaN            NaN            NaN            NaN   \n",
       "2            NaN            NaN            NaN            NaN            NaN   \n",
       "3            NaN            NaN            NaN            NaN            NaN   \n",
       "4            NaN            NaN            NaN            NaN            NaN   \n",
       "\n",
       "   STATUS_nan_MEAN  \n",
       "0              NaN  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4              NaN  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "bureau.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_uuid": "da92063a10f0751e64534bd7ab87b35cbffc3bae"
   },
   "outputs": [],
   "source": [
    "# Bureau and bureau_balance numeric features\n",
    "num_aggregations = {\n",
    "    'DAYS_CREDIT': ['min', 'max', 'mean', 'var'],\n",
    "    'DAYS_CREDIT_ENDDATE': ['min', 'max', 'mean'],\n",
    "    'DAYS_CREDIT_UPDATE': ['mean'],\n",
    "    'CREDIT_DAY_OVERDUE': ['max', 'mean'],\n",
    "    'AMT_CREDIT_MAX_OVERDUE': ['mean'],\n",
    "    'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],\n",
    "    'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],\n",
    "    'AMT_CREDIT_SUM_OVERDUE': ['mean'],\n",
    "    'AMT_CREDIT_SUM_LIMIT': ['mean', 'sum'],\n",
    "    'AMT_ANNUITY': ['max', 'mean'],\n",
    "    'CNT_CREDIT_PROLONG': ['sum'],\n",
    "#     'MONTHS_BALANCE_MIN': ['min'],\n",
    "#     'MONTHS_BALANCE_MAX': ['max'],\n",
    "#     'MONTHS_BALANCE_SIZE': ['mean', 'sum']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_uuid": "67985c92c5b50816ffd75b2e20ca8f04ae773fa9"
   },
   "outputs": [],
   "source": [
    "# Bureau and bureau_balance categorical features\n",
    "cat_aggregations = {}\n",
    "for cat in bureau_cat: cat_aggregations[cat] = ['mean']\n",
    "for cat in bb_cat: cat_aggregations[cat + \"_MEAN\"] = ['mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_uuid": "68c81f1338902f5179591eaaf934bc1602c85eed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CREDIT_ACTIVE_Active': ['mean'],\n",
       " 'CREDIT_ACTIVE_Bad debt': ['mean'],\n",
       " 'CREDIT_ACTIVE_Closed': ['mean'],\n",
       " 'CREDIT_ACTIVE_Sold': ['mean'],\n",
       " 'CREDIT_ACTIVE_nan': ['mean'],\n",
       " 'CREDIT_CURRENCY_currency 1': ['mean'],\n",
       " 'CREDIT_CURRENCY_currency 2': ['mean'],\n",
       " 'CREDIT_CURRENCY_currency 3': ['mean'],\n",
       " 'CREDIT_CURRENCY_currency 4': ['mean'],\n",
       " 'CREDIT_CURRENCY_nan': ['mean'],\n",
       " 'CREDIT_TYPE_Another type of loan': ['mean'],\n",
       " 'CREDIT_TYPE_Car loan': ['mean'],\n",
       " 'CREDIT_TYPE_Cash loan (non-earmarked)': ['mean'],\n",
       " 'CREDIT_TYPE_Consumer credit': ['mean'],\n",
       " 'CREDIT_TYPE_Credit card': ['mean'],\n",
       " 'CREDIT_TYPE_Interbank credit': ['mean'],\n",
       " 'CREDIT_TYPE_Loan for business development': ['mean'],\n",
       " 'CREDIT_TYPE_Loan for purchase of shares (margin lending)': ['mean'],\n",
       " 'CREDIT_TYPE_Loan for the purchase of equipment': ['mean'],\n",
       " 'CREDIT_TYPE_Loan for working capital replenishment': ['mean'],\n",
       " 'CREDIT_TYPE_Microloan': ['mean'],\n",
       " 'CREDIT_TYPE_Mobile operator loan': ['mean'],\n",
       " 'CREDIT_TYPE_Mortgage': ['mean'],\n",
       " 'CREDIT_TYPE_Real estate loan': ['mean'],\n",
       " 'CREDIT_TYPE_Unknown type of loan': ['mean'],\n",
       " 'CREDIT_TYPE_nan': ['mean'],\n",
       " 'STATUS_0_MEAN': ['mean'],\n",
       " 'STATUS_1_MEAN': ['mean'],\n",
       " 'STATUS_2_MEAN': ['mean'],\n",
       " 'STATUS_3_MEAN': ['mean'],\n",
       " 'STATUS_4_MEAN': ['mean'],\n",
       " 'STATUS_5_MEAN': ['mean'],\n",
       " 'STATUS_C_MEAN': ['mean'],\n",
       " 'STATUS_X_MEAN': ['mean'],\n",
       " 'STATUS_nan_MEAN': ['mean']}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_uuid": "374f9c25d54ba0e49771d4bfd2856cdbf07954ee"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>DAYS_CREDIT</th>\n",
       "      <th>CREDIT_DAY_OVERDUE</th>\n",
       "      <th>DAYS_CREDIT_ENDDATE</th>\n",
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       "      <th>...</th>\n",
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       "      <th>STATUS_2_MEAN</th>\n",
       "      <th>STATUS_3_MEAN</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>215354</td>\n",
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       "      <td>1197.0</td>\n",
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       "<p>5 rows × 50 columns</p>\n",
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      "text/plain": [
       "   SK_ID_CURR  DAYS_CREDIT  CREDIT_DAY_OVERDUE  DAYS_CREDIT_ENDDATE  \\\n",
       "0      215354         -497                   0               -153.0   \n",
       "1      215354         -208                   0               1075.0   \n",
       "2      215354         -203                   0                528.0   \n",
       "3      215354         -203                   0                  NaN   \n",
       "4      215354         -629                   0               1197.0   \n",
       "\n",
       "   DAYS_ENDDATE_FACT  AMT_CREDIT_MAX_OVERDUE  CNT_CREDIT_PROLONG  \\\n",
       "0             -153.0                     NaN                   0   \n",
       "1                NaN                     NaN                   0   \n",
       "2                NaN                     NaN                   0   \n",
       "3                NaN                     NaN                   0   \n",
       "4                NaN                 77674.5                   0   \n",
       "\n",
       "   AMT_CREDIT_SUM  AMT_CREDIT_SUM_DEBT  AMT_CREDIT_SUM_LIMIT       ...         \\\n",
       "0         91323.0                  0.0                   NaN       ...          \n",
       "1        225000.0             171342.0                   NaN       ...          \n",
       "2        464323.5                  NaN                   NaN       ...          \n",
       "3         90000.0                  NaN                   NaN       ...          \n",
       "4       2700000.0                  NaN                   NaN       ...          \n",
       "\n",
       "   SK_ID_BUREAU_MEAN  STATUS_0_MEAN  STATUS_1_MEAN  STATUS_2_MEAN  \\\n",
       "0                NaN            NaN            NaN            NaN   \n",
       "1                NaN            NaN            NaN            NaN   \n",
       "2                NaN            NaN            NaN            NaN   \n",
       "3                NaN            NaN            NaN            NaN   \n",
       "4                NaN            NaN            NaN            NaN   \n",
       "\n",
       "   STATUS_3_MEAN  STATUS_4_MEAN  STATUS_5_MEAN  STATUS_C_MEAN  STATUS_X_MEAN  \\\n",
       "0            NaN            NaN            NaN            NaN            NaN   \n",
       "1            NaN            NaN            NaN            NaN            NaN   \n",
       "2            NaN            NaN            NaN            NaN            NaN   \n",
       "3            NaN            NaN            NaN            NaN            NaN   \n",
       "4            NaN            NaN            NaN            NaN            NaN   \n",
       "\n",
       "   STATUS_nan_MEAN  \n",
       "0              NaN  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4              NaN  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_uuid": "b20ca0e80813d465a9c5a03874f4ccd402459d7f"
   },
   "outputs": [],
   "source": [
    "\n",
    "bureau_agg = bureau.groupby('SK_ID_CURR').agg({**num_aggregations, **cat_aggregations})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "42e81bfb695fbf19a45f190c443d5c3d9b3c075a"
   },
   "outputs": [
    {
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       "      <td>1778.0</td>\n",
       "      <td>82.428571</td>\n",
       "      <td>-93.142857</td>\n",
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       "      <td>0.007519</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.441240</td>\n",
       "      <td>0.214590</td>\n",
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       "    <tr>\n",
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       "      <td>-1437</td>\n",
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       "      <td>-2434.0</td>\n",
       "      <td>1216.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>-595.0</td>\n",
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       "      <td>-532.000000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-128.0</td>\n",
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      ],
      "text/plain": [
       "           DAYS_CREDIT                                  DAYS_CREDIT_ENDDATE  \\\n",
       "                   min  max         mean            var                 min   \n",
       "SK_ID_CURR                                                                    \n",
       "100001           -1572  -49  -735.000000  240043.666667             -1329.0   \n",
       "100002           -1437 -103  -874.000000  186150.000000             -1072.0   \n",
       "100003           -2586 -606 -1400.750000  827783.583333             -2434.0   \n",
       "100004           -1326 -408  -867.000000  421362.000000              -595.0   \n",
       "100005            -373  -62  -190.666667   26340.333333              -128.0   \n",
       "\n",
       "                               DAYS_CREDIT_UPDATE CREDIT_DAY_OVERDUE       \\\n",
       "               max        mean               mean                max mean   \n",
       "SK_ID_CURR                                                                  \n",
       "100001      1778.0   82.428571         -93.142857                  0  0.0   \n",
       "100002       780.0 -349.000000        -499.875000                  0  0.0   \n",
       "100003      1216.0 -544.500000        -816.000000                  0  0.0   \n",
       "100004      -382.0 -488.500000        -532.000000                  0  0.0   \n",
       "100005      1324.0  439.333333         -54.333333                  0  0.0   \n",
       "\n",
       "                 ...       CREDIT_TYPE_nan STATUS_0_MEAN STATUS_1_MEAN  \\\n",
       "                 ...                  mean          mean          mean   \n",
       "SK_ID_CURR       ...                                                     \n",
       "100001           ...                     0      0.336651      0.007519   \n",
       "100002           ...                     0      0.406960      0.255682   \n",
       "100003           ...                     0           NaN           NaN   \n",
       "100004           ...                     0           NaN           NaN   \n",
       "100005           ...                     0      0.735043      0.000000   \n",
       "\n",
       "           STATUS_2_MEAN STATUS_3_MEAN STATUS_4_MEAN STATUS_5_MEAN  \\\n",
       "                    mean          mean          mean          mean   \n",
       "SK_ID_CURR                                                           \n",
       "100001               0.0           0.0           0.0           0.0   \n",
       "100002               0.0           0.0           0.0           0.0   \n",
       "100003               NaN           NaN           NaN           NaN   \n",
       "100004               NaN           NaN           NaN           NaN   \n",
       "100005               0.0           0.0           0.0           0.0   \n",
       "\n",
       "           STATUS_C_MEAN STATUS_X_MEAN STATUS_nan_MEAN  \n",
       "                    mean          mean            mean  \n",
       "SK_ID_CURR                                              \n",
       "100001          0.441240      0.214590             0.0  \n",
       "100002          0.175426      0.161932             0.0  \n",
       "100003               NaN           NaN             NaN  \n",
       "100004               NaN           NaN             NaN  \n",
       "100005          0.128205      0.136752             0.0  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "db1f80cd676b7d950bd71b6f946d3a693d2699eb"
   },
   "outputs": [],
   "source": [
    "bureau_agg.columns = pd.Index(['BURO_' + e[0] + \"_\" + e[1].upper() for e in bureau_agg.columns.tolist()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "59895d9dac2582761b96dab2e98c82d0821c1a7b"
   },
   "outputs": [
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       "            BURO_DAYS_CREDIT_MIN  BURO_DAYS_CREDIT_MAX  BURO_DAYS_CREDIT_MEAN  \\\n",
       "SK_ID_CURR                                                                      \n",
       "100001                     -1572                   -49            -735.000000   \n",
       "100002                     -1437                  -103            -874.000000   \n",
       "100003                     -2586                  -606           -1400.750000   \n",
       "100004                     -1326                  -408            -867.000000   \n",
       "100005                      -373                   -62            -190.666667   \n",
       "\n",
       "            BURO_DAYS_CREDIT_VAR  BURO_DAYS_CREDIT_ENDDATE_MIN  \\\n",
       "SK_ID_CURR                                                       \n",
       "100001             240043.666667                       -1329.0   \n",
       "100002             186150.000000                       -1072.0   \n",
       "100003             827783.583333                       -2434.0   \n",
       "100004             421362.000000                        -595.0   \n",
       "100005              26340.333333                        -128.0   \n",
       "\n",
       "            BURO_DAYS_CREDIT_ENDDATE_MAX  BURO_DAYS_CREDIT_ENDDATE_MEAN  \\\n",
       "SK_ID_CURR                                                                \n",
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       "100002                             780.0                    -349.000000   \n",
       "100003                            1216.0                    -544.500000   \n",
       "100004                            -382.0                    -488.500000   \n",
       "100005                            1324.0                     439.333333   \n",
       "\n",
       "            BURO_DAYS_CREDIT_UPDATE_MEAN  BURO_CREDIT_DAY_OVERDUE_MAX  \\\n",
       "SK_ID_CURR                                                              \n",
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       "100002                       -499.875000                            0   \n",
       "100003                       -816.000000                            0   \n",
       "100004                       -532.000000                            0   \n",
       "100005                        -54.333333                            0   \n",
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       "SK_ID_CURR                                          ...               \n",
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       "100003                               0.0            ...               \n",
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       "SK_ID_CURR                                                       \n",
       "100001                              0                 0.336651   \n",
       "100002                              0                 0.406960   \n",
       "100003                              0                      NaN   \n",
       "100004                              0                      NaN   \n",
       "100005                              0                 0.735043   \n",
       "\n",
       "            BURO_STATUS_1_MEAN_MEAN  BURO_STATUS_2_MEAN_MEAN  \\\n",
       "SK_ID_CURR                                                     \n",
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       "100003                          NaN                      NaN   \n",
       "100004                          NaN                      NaN   \n",
       "100005                     0.000000                      0.0   \n",
       "\n",
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       "SK_ID_CURR                                                     \n",
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       "SK_ID_CURR                                                     \n",
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       "100002                          0.0                 0.175426   \n",
       "100003                          NaN                      NaN   \n",
       "100004                          NaN                      NaN   \n",
       "100005                          0.0                 0.128205   \n",
       "\n",
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       "SK_ID_CURR                                                      \n",
       "100001                     0.214590                        0.0  \n",
       "100002                     0.161932                        0.0  \n",
       "100003                          NaN                        NaN  \n",
       "100004                          NaN                        NaN  \n",
       "100005                     0.136752                        0.0  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "4c7aec635a35229a84c23732050de0703425b25e"
   },
   "outputs": [],
   "source": [
    "active = bureau[bureau['CREDIT_ACTIVE_Active'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "8c8ce629b2cb728db929ba596a99758d86f29729"
   },
   "outputs": [
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       "      <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 × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  DAYS_CREDIT  CREDIT_DAY_OVERDUE  DAYS_CREDIT_ENDDATE  \\\n",
       "1      215354         -208                   0               1075.0   \n",
       "2      215354         -203                   0                528.0   \n",
       "3      215354         -203                   0                  NaN   \n",
       "4      215354         -629                   0               1197.0   \n",
       "5      215354         -273                   0              27460.0   \n",
       "\n",
       "   DAYS_ENDDATE_FACT  AMT_CREDIT_MAX_OVERDUE  CNT_CREDIT_PROLONG  \\\n",
       "1                NaN                     NaN                   0   \n",
       "2                NaN                     NaN                   0   \n",
       "3                NaN                     NaN                   0   \n",
       "4                NaN                 77674.5                   0   \n",
       "5                NaN                     0.0                   0   \n",
       "\n",
       "   AMT_CREDIT_SUM  AMT_CREDIT_SUM_DEBT  AMT_CREDIT_SUM_LIMIT       ...         \\\n",
       "1        225000.0            171342.00                   NaN       ...          \n",
       "2        464323.5                  NaN                   NaN       ...          \n",
       "3         90000.0                  NaN                   NaN       ...          \n",
       "4       2700000.0                  NaN                   NaN       ...          \n",
       "5        180000.0             71017.38             108982.62       ...          \n",
       "\n",
       "   SK_ID_BUREAU_MEAN  STATUS_0_MEAN  STATUS_1_MEAN  STATUS_2_MEAN  \\\n",
       "1                NaN            NaN            NaN            NaN   \n",
       "2                NaN            NaN            NaN            NaN   \n",
       "3                NaN            NaN            NaN            NaN   \n",
       "4                NaN            NaN            NaN            NaN   \n",
       "5                NaN            NaN            NaN            NaN   \n",
       "\n",
       "   STATUS_3_MEAN  STATUS_4_MEAN  STATUS_5_MEAN  STATUS_C_MEAN  STATUS_X_MEAN  \\\n",
       "1            NaN            NaN            NaN            NaN            NaN   \n",
       "2            NaN            NaN            NaN            NaN            NaN   \n",
       "3            NaN            NaN            NaN            NaN            NaN   \n",
       "4            NaN            NaN            NaN            NaN            NaN   \n",
       "5            NaN            NaN            NaN            NaN            NaN   \n",
       "\n",
       "   STATUS_nan_MEAN  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4              NaN  \n",
       "5              NaN  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_uuid": "f01f69b4bc0834d87e59964f0c7edd3c7711d644"
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   "outputs": [
    {
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      ],
      "text/plain": [
       "           DAYS_CREDIT                                 DAYS_CREDIT_ENDDATE  \\\n",
       "                   min  max        mean            var                 min   \n",
       "SK_ID_CURR                                                                   \n",
       "100001            -559  -49 -309.333333   65110.333333               411.0   \n",
       "100002           -1042 -103 -572.500000  440860.500000               780.0   \n",
       "100003            -606 -606 -606.000000            NaN              1216.0   \n",
       "100005            -137  -62  -99.500000    2812.500000               122.0   \n",
       "100008             -78  -78  -78.000000            NaN               471.0   \n",
       "\n",
       "                                DAYS_CREDIT_UPDATE CREDIT_DAY_OVERDUE       \\\n",
       "               max         mean               mean                max mean   \n",
       "SK_ID_CURR                                                                   \n",
       "100001      1778.0  1030.333333         -10.666667                  0  0.0   \n",
       "100002       780.0   780.000000         -15.500000                  0  0.0   \n",
       "100003      1216.0  1216.000000         -43.000000                  0  0.0   \n",
       "100005      1324.0   723.000000         -21.000000                  0  0.0   \n",
       "100008       471.0   471.000000         -16.000000                  0  0.0   \n",
       "\n",
       "                  ...         AMT_CREDIT_SUM AMT_CREDIT_SUM_DEBT             \\\n",
       "                  ...                    sum                 max       mean   \n",
       "SK_ID_CURR        ...                                                         \n",
       "100001            ...             884025.000            373239.0  198895.50   \n",
       "100002            ...             481988.565            245781.0  122890.50   \n",
       "100003            ...             810000.000                 0.0       0.00   \n",
       "100005            ...             598626.000            543087.0  284204.25   \n",
       "100008            ...             267606.000            240057.0  240057.00   \n",
       "\n",
       "                     AMT_CREDIT_SUM_OVERDUE AMT_CREDIT_SUM_LIMIT              \\\n",
       "                 sum                   mean                 mean         sum   \n",
       "SK_ID_CURR                                                                     \n",
       "100001      596686.5                    0.0               0.0000       0.000   \n",
       "100002      245781.0                    0.0           15994.2825   31988.565   \n",
       "100003           0.0                    0.0          810000.0000  810000.000   \n",
       "100005      568408.5                    0.0               0.0000       0.000   \n",
       "100008      240057.0                    0.0               0.0000       0.000   \n",
       "\n",
       "           AMT_ANNUITY          CNT_CREDIT_PROLONG  \n",
       "                   max     mean                sum  \n",
       "SK_ID_CURR                                          \n",
       "100001         10822.5  8272.50                  0  \n",
       "100002             0.0     0.00                  0  \n",
       "100003             NaN      NaN                  0  \n",
       "100005          4261.5  2130.75                  0  \n",
       "100008             NaN      NaN                  0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_agg = active.groupby('SK_ID_CURR').agg(num_aggregations)\n",
    "active_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "281442105191bc2bce775c3e9f6b36987761648b"
   },
   "outputs": [],
   "source": [
    "active_agg.columns = pd.Index(['ACTIVE_' + e[0] + \"_\" + e[1].upper() for e in active_agg.columns.tolist()])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_uuid": "41e1e7eedb87f74cdfe77eb48e8430c0bb4d7600"
   },
   "outputs": [
    {
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       "      <td>-62</td>\n",
       "      <td>-99.500000</td>\n",
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       "      <td>122.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>723.000000</td>\n",
       "      <td>-21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>100008</th>\n",
       "      <td>-78</td>\n",
       "      <td>-78</td>\n",
       "      <td>-78.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>471.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>471.000000</td>\n",
       "      <td>-16.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>267606.000</td>\n",
       "      <td>240057.0</td>\n",
       "      <td>240057.00</td>\n",
       "      <td>240057.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ACTIVE_DAYS_CREDIT_MIN  ACTIVE_DAYS_CREDIT_MAX  \\\n",
       "SK_ID_CURR                                                   \n",
       "100001                        -559                     -49   \n",
       "100002                       -1042                    -103   \n",
       "100003                        -606                    -606   \n",
       "100005                        -137                     -62   \n",
       "100008                         -78                     -78   \n",
       "\n",
       "            ACTIVE_DAYS_CREDIT_MEAN  ACTIVE_DAYS_CREDIT_VAR  \\\n",
       "SK_ID_CURR                                                    \n",
       "100001                  -309.333333            65110.333333   \n",
       "100002                  -572.500000           440860.500000   \n",
       "100003                  -606.000000                     NaN   \n",
       "100005                   -99.500000             2812.500000   \n",
       "100008                   -78.000000                     NaN   \n",
       "\n",
       "            ACTIVE_DAYS_CREDIT_ENDDATE_MIN  ACTIVE_DAYS_CREDIT_ENDDATE_MAX  \\\n",
       "SK_ID_CURR                                                                   \n",
       "100001                               411.0                          1778.0   \n",
       "100002                               780.0                           780.0   \n",
       "100003                              1216.0                          1216.0   \n",
       "100005                               122.0                          1324.0   \n",
       "100008                               471.0                           471.0   \n",
       "\n",
       "            ACTIVE_DAYS_CREDIT_ENDDATE_MEAN  ACTIVE_DAYS_CREDIT_UPDATE_MEAN  \\\n",
       "SK_ID_CURR                                                                    \n",
       "100001                          1030.333333                      -10.666667   \n",
       "100002                           780.000000                      -15.500000   \n",
       "100003                          1216.000000                      -43.000000   \n",
       "100005                           723.000000                      -21.000000   \n",
       "100008                           471.000000                      -16.000000   \n",
       "\n",
       "            ACTIVE_CREDIT_DAY_OVERDUE_MAX  ACTIVE_CREDIT_DAY_OVERDUE_MEAN  \\\n",
       "SK_ID_CURR                                                                  \n",
       "100001                                  0                             0.0   \n",
       "100002                                  0                             0.0   \n",
       "100003                                  0                             0.0   \n",
       "100005                                  0                             0.0   \n",
       "100008                                  0                             0.0   \n",
       "\n",
       "                        ...                ACTIVE_AMT_CREDIT_SUM_SUM  \\\n",
       "SK_ID_CURR              ...                                            \n",
       "100001                  ...                               884025.000   \n",
       "100002                  ...                               481988.565   \n",
       "100003                  ...                               810000.000   \n",
       "100005                  ...                               598626.000   \n",
       "100008                  ...                               267606.000   \n",
       "\n",
       "            ACTIVE_AMT_CREDIT_SUM_DEBT_MAX  ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN  \\\n",
       "SK_ID_CURR                                                                    \n",
       "100001                            373239.0                        198895.50   \n",
       "100002                            245781.0                        122890.50   \n",
       "100003                                 0.0                             0.00   \n",
       "100005                            543087.0                        284204.25   \n",
       "100008                            240057.0                        240057.00   \n",
       "\n",
       "            ACTIVE_AMT_CREDIT_SUM_DEBT_SUM  \\\n",
       "SK_ID_CURR                                   \n",
       "100001                            596686.5   \n",
       "100002                            245781.0   \n",
       "100003                                 0.0   \n",
       "100005                            568408.5   \n",
       "100008                            240057.0   \n",
       "\n",
       "            ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN  \\\n",
       "SK_ID_CURR                                       \n",
       "100001                                     0.0   \n",
       "100002                                     0.0   \n",
       "100003                                     0.0   \n",
       "100005                                     0.0   \n",
       "100008                                     0.0   \n",
       "\n",
       "            ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN  ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM  \\\n",
       "SK_ID_CURR                                                                      \n",
       "100001                                0.0000                            0.000   \n",
       "100002                            15994.2825                        31988.565   \n",
       "100003                           810000.0000                       810000.000   \n",
       "100005                                0.0000                            0.000   \n",
       "100008                                0.0000                            0.000   \n",
       "\n",
       "            ACTIVE_AMT_ANNUITY_MAX  ACTIVE_AMT_ANNUITY_MEAN  \\\n",
       "SK_ID_CURR                                                    \n",
       "100001                     10822.5                  8272.50   \n",
       "100002                         0.0                     0.00   \n",
       "100003                         NaN                      NaN   \n",
       "100005                      4261.5                  2130.75   \n",
       "100008                         NaN                      NaN   \n",
       "\n",
       "            ACTIVE_CNT_CREDIT_PROLONG_SUM  \n",
       "SK_ID_CURR                                 \n",
       "100001                                  0  \n",
       "100002                                  0  \n",
       "100003                                  0  \n",
       "100005                                  0  \n",
       "100008                                  0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_uuid": "3ec4a9aec643f8a53e2c7abde7a2ee77dddc102c"
   },
   "outputs": [],
   "source": [
    "bureau_agg = bureau_agg.join(active_agg, how='left', on='SK_ID_CURR')\n",
    "del active, active_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_uuid": "0cbf994772d3f0d40526128b46fcb89d641e2a47"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BURO_DAYS_CREDIT_MIN</th>\n",
       "      <th>BURO_DAYS_CREDIT_MAX</th>\n",
       "      <th>BURO_DAYS_CREDIT_MEAN</th>\n",
       "      <th>BURO_DAYS_CREDIT_VAR</th>\n",
       "      <th>BURO_DAYS_CREDIT_ENDDATE_MIN</th>\n",
       "      <th>BURO_DAYS_CREDIT_ENDDATE_MAX</th>\n",
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       "      <th>BURO_CREDIT_DAY_OVERDUE_MEAN</th>\n",
       "      <th>...</th>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_SUM</th>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_DEBT_MAX</th>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN</th>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_DEBT_SUM</th>\n",
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       "    </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",
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       "  <tbody>\n",
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       "      <th>100001</th>\n",
       "      <td>-1572</td>\n",
       "      <td>-49</td>\n",
       "      <td>-735.000000</td>\n",
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       "      <td>-93.142857</td>\n",
       "      <td>0</td>\n",
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       "      <th>100002</th>\n",
       "      <td>-1437</td>\n",
       "      <td>-103</td>\n",
       "      <td>-874.000000</td>\n",
       "      <td>186150.000000</td>\n",
       "      <td>-1072.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>-349.000000</td>\n",
       "      <td>-499.875000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>481988.565</td>\n",
       "      <td>245781.0</td>\n",
       "      <td>122890.50</td>\n",
       "      <td>245781.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>-2586</td>\n",
       "      <td>-606</td>\n",
       "      <td>-1400.750000</td>\n",
       "      <td>827783.583333</td>\n",
       "      <td>-2434.0</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>-544.500000</td>\n",
       "      <td>-816.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>810000.000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>810000.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>-1326</td>\n",
       "      <td>-408</td>\n",
       "      <td>-867.000000</td>\n",
       "      <td>421362.000000</td>\n",
       "      <td>-595.0</td>\n",
       "      <td>-382.0</td>\n",
       "      <td>-488.500000</td>\n",
       "      <td>-532.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            BURO_DAYS_CREDIT_MIN  BURO_DAYS_CREDIT_MAX  BURO_DAYS_CREDIT_MEAN  \\\n",
       "SK_ID_CURR                                                                      \n",
       "100001                     -1572                   -49            -735.000000   \n",
       "100002                     -1437                  -103            -874.000000   \n",
       "100003                     -2586                  -606           -1400.750000   \n",
       "100004                     -1326                  -408            -867.000000   \n",
       "100005                      -373                   -62            -190.666667   \n",
       "\n",
       "            BURO_DAYS_CREDIT_VAR  BURO_DAYS_CREDIT_ENDDATE_MIN  \\\n",
       "SK_ID_CURR                                                       \n",
       "100001             240043.666667                       -1329.0   \n",
       "100002             186150.000000                       -1072.0   \n",
       "100003             827783.583333                       -2434.0   \n",
       "100004             421362.000000                        -595.0   \n",
       "100005              26340.333333                        -128.0   \n",
       "\n",
       "            BURO_DAYS_CREDIT_ENDDATE_MAX  BURO_DAYS_CREDIT_ENDDATE_MEAN  \\\n",
       "SK_ID_CURR                                                                \n",
       "100001                            1778.0                      82.428571   \n",
       "100002                             780.0                    -349.000000   \n",
       "100003                            1216.0                    -544.500000   \n",
       "100004                            -382.0                    -488.500000   \n",
       "100005                            1324.0                     439.333333   \n",
       "\n",
       "            BURO_DAYS_CREDIT_UPDATE_MEAN  BURO_CREDIT_DAY_OVERDUE_MAX  \\\n",
       "SK_ID_CURR                                                              \n",
       "100001                        -93.142857                            0   \n",
       "100002                       -499.875000                            0   \n",
       "100003                       -816.000000                            0   \n",
       "100004                       -532.000000                            0   \n",
       "100005                        -54.333333                            0   \n",
       "\n",
       "            BURO_CREDIT_DAY_OVERDUE_MEAN              ...                \\\n",
       "SK_ID_CURR                                            ...                 \n",
       "100001                               0.0              ...                 \n",
       "100002                               0.0              ...                 \n",
       "100003                               0.0              ...                 \n",
       "100004                               0.0              ...                 \n",
       "100005                               0.0              ...                 \n",
       "\n",
       "            ACTIVE_AMT_CREDIT_SUM_SUM  ACTIVE_AMT_CREDIT_SUM_DEBT_MAX  \\\n",
       "SK_ID_CURR                                                              \n",
       "100001                     884025.000                        373239.0   \n",
       "100002                     481988.565                        245781.0   \n",
       "100003                     810000.000                             0.0   \n",
       "100004                            NaN                             NaN   \n",
       "100005                     598626.000                        543087.0   \n",
       "\n",
       "            ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN  ACTIVE_AMT_CREDIT_SUM_DEBT_SUM  \\\n",
       "SK_ID_CURR                                                                    \n",
       "100001                            198895.50                        596686.5   \n",
       "100002                            122890.50                        245781.0   \n",
       "100003                                 0.00                             0.0   \n",
       "100004                                  NaN                             NaN   \n",
       "100005                            284204.25                        568408.5   \n",
       "\n",
       "            ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN  \\\n",
       "SK_ID_CURR                                       \n",
       "100001                                     0.0   \n",
       "100002                                     0.0   \n",
       "100003                                     0.0   \n",
       "100004                                     NaN   \n",
       "100005                                     0.0   \n",
       "\n",
       "            ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN  ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM  \\\n",
       "SK_ID_CURR                                                                      \n",
       "100001                                0.0000                            0.000   \n",
       "100002                            15994.2825                        31988.565   \n",
       "100003                           810000.0000                       810000.000   \n",
       "100004                                   NaN                              NaN   \n",
       "100005                                0.0000                            0.000   \n",
       "\n",
       "            ACTIVE_AMT_ANNUITY_MAX  ACTIVE_AMT_ANNUITY_MEAN  \\\n",
       "SK_ID_CURR                                                    \n",
       "100001                     10822.5                  8272.50   \n",
       "100002                         0.0                     0.00   \n",
       "100003                         NaN                      NaN   \n",
       "100004                         NaN                      NaN   \n",
       "100005                      4261.5                  2130.75   \n",
       "\n",
       "            ACTIVE_CNT_CREDIT_PROLONG_SUM  \n",
       "SK_ID_CURR                                 \n",
       "100001                                0.0  \n",
       "100002                                0.0  \n",
       "100003                                0.0  \n",
       "100004                                NaN  \n",
       "100005                                0.0  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_uuid": "e00ba5f8f328a4a07b5185ba77f3d615ff03f256"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305811, 81)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_uuid": "eea12a553546cfc83f071945ba5cf1b1d97eda8f"
   },
   "outputs": [],
   "source": [
    "# Bureau: Closed credits - using only numerical aggregations\n",
    "closed = bureau[bureau['CREDIT_ACTIVE_Closed'] == 1]\n",
    "closed_agg = closed.groupby('SK_ID_CURR').agg(num_aggregations)\n",
    "closed_agg.columns = pd.Index(['CLOSED_' + e[0] + \"_\" + e[1].upper() for e in closed_agg.columns.tolist()])\n",
    "bureau_agg = bureau_agg.join(closed_agg, how='left', on='SK_ID_CURR')\n",
    "del closed, closed_agg, bureau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_uuid": "e69794d4c55675b2a24be0266a1cfd323361273d"
   },
   "outputs": [
    {
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       "      <th>BURO_DAYS_CREDIT_MEAN</th>\n",
       "      <th>BURO_DAYS_CREDIT_VAR</th>\n",
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       "      <th>BURO_CREDIT_DAY_OVERDUE_MEAN</th>\n",
       "      <th>...</th>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_SUM</th>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_DEBT_MAX</th>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_DEBT_MEAN</th>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_DEBT_SUM</th>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_OVERDUE_MEAN</th>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_LIMIT_MEAN</th>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_LIMIT_SUM</th>\n",
       "      <th>CLOSED_AMT_ANNUITY_MAX</th>\n",
       "      <th>CLOSED_AMT_ANNUITY_MEAN</th>\n",
       "      <th>CLOSED_CNT_CREDIT_PROLONG_SUM</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>-1572</td>\n",
       "      <td>-49</td>\n",
       "      <td>-735.000000</td>\n",
       "      <td>240043.666667</td>\n",
       "      <td>-1329.0</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>82.428571</td>\n",
       "      <td>-93.142857</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>569340.0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>-1437</td>\n",
       "      <td>-103</td>\n",
       "      <td>-874.000000</td>\n",
       "      <td>186150.000000</td>\n",
       "      <td>-1072.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>-349.000000</td>\n",
       "      <td>-499.875000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>383067.0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>-2586</td>\n",
       "      <td>-606</td>\n",
       "      <td>-1400.750000</td>\n",
       "      <td>827783.583333</td>\n",
       "      <td>-2434.0</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>-544.500000</td>\n",
       "      <td>-816.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>207400.5</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>-1326</td>\n",
       "      <td>-408</td>\n",
       "      <td>-867.000000</td>\n",
       "      <td>421362.000000</td>\n",
       "      <td>-595.0</td>\n",
       "      <td>-382.0</td>\n",
       "      <td>-488.500000</td>\n",
       "      <td>-532.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>189037.8</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>-373</td>\n",
       "      <td>-62</td>\n",
       "      <td>-190.666667</td>\n",
       "      <td>26340.333333</td>\n",
       "      <td>-128.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>439.333333</td>\n",
       "      <td>-54.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>58500.0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            BURO_DAYS_CREDIT_MIN  BURO_DAYS_CREDIT_MAX  BURO_DAYS_CREDIT_MEAN  \\\n",
       "SK_ID_CURR                                                                      \n",
       "100001                     -1572                   -49            -735.000000   \n",
       "100002                     -1437                  -103            -874.000000   \n",
       "100003                     -2586                  -606           -1400.750000   \n",
       "100004                     -1326                  -408            -867.000000   \n",
       "100005                      -373                   -62            -190.666667   \n",
       "\n",
       "            BURO_DAYS_CREDIT_VAR  BURO_DAYS_CREDIT_ENDDATE_MIN  \\\n",
       "SK_ID_CURR                                                       \n",
       "100001             240043.666667                       -1329.0   \n",
       "100002             186150.000000                       -1072.0   \n",
       "100003             827783.583333                       -2434.0   \n",
       "100004             421362.000000                        -595.0   \n",
       "100005              26340.333333                        -128.0   \n",
       "\n",
       "            BURO_DAYS_CREDIT_ENDDATE_MAX  BURO_DAYS_CREDIT_ENDDATE_MEAN  \\\n",
       "SK_ID_CURR                                                                \n",
       "100001                            1778.0                      82.428571   \n",
       "100002                             780.0                    -349.000000   \n",
       "100003                            1216.0                    -544.500000   \n",
       "100004                            -382.0                    -488.500000   \n",
       "100005                            1324.0                     439.333333   \n",
       "\n",
       "            BURO_DAYS_CREDIT_UPDATE_MEAN  BURO_CREDIT_DAY_OVERDUE_MAX  \\\n",
       "SK_ID_CURR                                                              \n",
       "100001                        -93.142857                            0   \n",
       "100002                       -499.875000                            0   \n",
       "100003                       -816.000000                            0   \n",
       "100004                       -532.000000                            0   \n",
       "100005                        -54.333333                            0   \n",
       "\n",
       "            BURO_CREDIT_DAY_OVERDUE_MEAN              ...                \\\n",
       "SK_ID_CURR                                            ...                 \n",
       "100001                               0.0              ...                 \n",
       "100002                               0.0              ...                 \n",
       "100003                               0.0              ...                 \n",
       "100004                               0.0              ...                 \n",
       "100005                               0.0              ...                 \n",
       "\n",
       "            CLOSED_AMT_CREDIT_SUM_SUM  CLOSED_AMT_CREDIT_SUM_DEBT_MAX  \\\n",
       "SK_ID_CURR                                                              \n",
       "100001                       569340.0                             0.0   \n",
       "100002                       383067.0                             0.0   \n",
       "100003                       207400.5                             0.0   \n",
       "100004                       189037.8                             0.0   \n",
       "100005                        58500.0                             0.0   \n",
       "\n",
       "            CLOSED_AMT_CREDIT_SUM_DEBT_MEAN  CLOSED_AMT_CREDIT_SUM_DEBT_SUM  \\\n",
       "SK_ID_CURR                                                                    \n",
       "100001                                  0.0                             0.0   \n",
       "100002                                  0.0                             0.0   \n",
       "100003                                  0.0                             0.0   \n",
       "100004                                  0.0                             0.0   \n",
       "100005                                  0.0                             0.0   \n",
       "\n",
       "            CLOSED_AMT_CREDIT_SUM_OVERDUE_MEAN  \\\n",
       "SK_ID_CURR                                       \n",
       "100001                                     0.0   \n",
       "100002                                     0.0   \n",
       "100003                                     0.0   \n",
       "100004                                     0.0   \n",
       "100005                                     0.0   \n",
       "\n",
       "            CLOSED_AMT_CREDIT_SUM_LIMIT_MEAN  CLOSED_AMT_CREDIT_SUM_LIMIT_SUM  \\\n",
       "SK_ID_CURR                                                                      \n",
       "100001                                   0.0                              0.0   \n",
       "100002                                   0.0                              0.0   \n",
       "100003                                   0.0                              0.0   \n",
       "100004                                   0.0                              0.0   \n",
       "100005                                   0.0                              0.0   \n",
       "\n",
       "            CLOSED_AMT_ANNUITY_MAX  CLOSED_AMT_ANNUITY_MEAN  \\\n",
       "SK_ID_CURR                                                    \n",
       "100001                         0.0                      0.0   \n",
       "100002                         0.0                      0.0   \n",
       "100003                         NaN                      NaN   \n",
       "100004                         NaN                      NaN   \n",
       "100005                         0.0                      0.0   \n",
       "\n",
       "            CLOSED_CNT_CREDIT_PROLONG_SUM  \n",
       "SK_ID_CURR                                 \n",
       "100001                                0.0  \n",
       "100002                                0.0  \n",
       "100003                                0.0  \n",
       "100004                                0.0  \n",
       "100005                                0.0  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_uuid": "58243aa7348bbc13a693a4883d3c7b5b13359d26"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305811, 104)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_uuid": "d06cc1993d3200b1ce5b06ad2798d83a172e9c21"
   },
   "outputs": [],
   "source": [
    "prev = pd.read_csv('previous_application.csv')\n",
    "prev, cat_cols = one_hot_encoder(prev, nan_as_category= True)\n",
    "# Days 365243 values -> nan\n",
    "prev['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True)\n",
    "prev['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace= True)\n",
    "prev['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace= True)\n",
    "prev['DAYS_LAST_DUE'].replace(365243, np.nan, inplace= True)\n",
    "prev['DAYS_TERMINATION'].replace(365243, np.nan, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_uuid": "9e371d3c456b854abddbd1af17bde454db8ecfd3"
   },
   "outputs": [],
   "source": [
    "# Add feature: value ask / value received percentage\n",
    "prev['APP_CREDIT_PERC'] = prev['AMT_APPLICATION'] / prev['AMT_CREDIT']\n",
    "# Previous applications numeric features\n",
    "num_aggregations = {\n",
    "    'AMT_ANNUITY': ['min', 'max', 'mean'],\n",
    "    'AMT_APPLICATION': ['min', 'max', 'mean'],\n",
    "    'AMT_CREDIT': ['min', 'max', 'mean'],\n",
    "    'APP_CREDIT_PERC': ['min', 'max', 'mean', 'var'],\n",
    "    'AMT_DOWN_PAYMENT': ['min', 'max', 'mean'],\n",
    "    'AMT_GOODS_PRICE': ['min', 'max', 'mean'],\n",
    "    'HOUR_APPR_PROCESS_START': ['min', 'max', 'mean'],\n",
    "    'RATE_DOWN_PAYMENT': ['min', 'max', 'mean'],\n",
    "    'DAYS_DECISION': ['min', 'max', 'mean'],\n",
    "    'CNT_PAYMENT': ['mean', 'sum'],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_uuid": "7b82e328c513b2bf36d9ce631913b35af65f16f3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1670214, 181)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_uuid": "9dc6f3b38cf13bfe53f08c9fdfe38d5907b38e1c"
   },
   "outputs": [],
   "source": [
    "cat_aggregations = {}\n",
    "for cat in cat_cols:\n",
    "    cat_aggregations[cat] = ['mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "_uuid": "8ea43b89e119cb1bd2c422175b302e28b09a1672"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NAME_CONTRACT_TYPE_Cash loans': ['mean'],\n",
       " 'NAME_CONTRACT_TYPE_Consumer loans': ['mean'],\n",
       " 'NAME_CONTRACT_TYPE_Revolving loans': ['mean'],\n",
       " 'NAME_CONTRACT_TYPE_XNA': ['mean'],\n",
       " 'NAME_CONTRACT_TYPE_nan': ['mean'],\n",
       " 'WEEKDAY_APPR_PROCESS_START_FRIDAY': ['mean'],\n",
       " 'WEEKDAY_APPR_PROCESS_START_MONDAY': ['mean'],\n",
       " 'WEEKDAY_APPR_PROCESS_START_SATURDAY': ['mean'],\n",
       " 'WEEKDAY_APPR_PROCESS_START_SUNDAY': ['mean'],\n",
       " 'WEEKDAY_APPR_PROCESS_START_THURSDAY': ['mean'],\n",
       " 'WEEKDAY_APPR_PROCESS_START_TUESDAY': ['mean'],\n",
       " 'WEEKDAY_APPR_PROCESS_START_WEDNESDAY': ['mean'],\n",
       " 'WEEKDAY_APPR_PROCESS_START_nan': ['mean'],\n",
       " 'FLAG_LAST_APPL_PER_CONTRACT_N': ['mean'],\n",
       " 'FLAG_LAST_APPL_PER_CONTRACT_Y': ['mean'],\n",
       " 'FLAG_LAST_APPL_PER_CONTRACT_nan': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Building a house or an annex': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Business development': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Buying a garage': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Buying a home': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Buying a new car': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Buying a used car': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Car repairs': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Education': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Everyday expenses': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Furniture': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Gasification / water supply': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Hobby': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Journey': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Medicine': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Money for a third person': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Other': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Payments on other loans': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Refusal to name the goal': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Repairs': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Urgent needs': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_XAP': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_XNA': ['mean'],\n",
       " 'NAME_CASH_LOAN_PURPOSE_nan': ['mean'],\n",
       " 'NAME_CONTRACT_STATUS_Approved': ['mean'],\n",
       " 'NAME_CONTRACT_STATUS_Canceled': ['mean'],\n",
       " 'NAME_CONTRACT_STATUS_Refused': ['mean'],\n",
       " 'NAME_CONTRACT_STATUS_Unused offer': ['mean'],\n",
       " 'NAME_CONTRACT_STATUS_nan': ['mean'],\n",
       " 'NAME_PAYMENT_TYPE_Cash through the bank': ['mean'],\n",
       " 'NAME_PAYMENT_TYPE_Cashless from the account of the employer': ['mean'],\n",
       " 'NAME_PAYMENT_TYPE_Non-cash from your account': ['mean'],\n",
       " 'NAME_PAYMENT_TYPE_XNA': ['mean'],\n",
       " 'NAME_PAYMENT_TYPE_nan': ['mean'],\n",
       " 'CODE_REJECT_REASON_CLIENT': ['mean'],\n",
       " 'CODE_REJECT_REASON_HC': ['mean'],\n",
       " 'CODE_REJECT_REASON_LIMIT': ['mean'],\n",
       " 'CODE_REJECT_REASON_SCO': ['mean'],\n",
       " 'CODE_REJECT_REASON_SCOFR': ['mean'],\n",
       " 'CODE_REJECT_REASON_SYSTEM': ['mean'],\n",
       " 'CODE_REJECT_REASON_VERIF': ['mean'],\n",
       " 'CODE_REJECT_REASON_XAP': ['mean'],\n",
       " 'CODE_REJECT_REASON_XNA': ['mean'],\n",
       " 'CODE_REJECT_REASON_nan': ['mean'],\n",
       " 'NAME_TYPE_SUITE_Children': ['mean'],\n",
       " 'NAME_TYPE_SUITE_Family': ['mean'],\n",
       " 'NAME_TYPE_SUITE_Group of people': ['mean'],\n",
       " 'NAME_TYPE_SUITE_Other_A': ['mean'],\n",
       " 'NAME_TYPE_SUITE_Other_B': ['mean'],\n",
       " 'NAME_TYPE_SUITE_Spouse, partner': ['mean'],\n",
       " 'NAME_TYPE_SUITE_Unaccompanied': ['mean'],\n",
       " 'NAME_TYPE_SUITE_nan': ['mean'],\n",
       " 'NAME_CLIENT_TYPE_New': ['mean'],\n",
       " 'NAME_CLIENT_TYPE_Refreshed': ['mean'],\n",
       " 'NAME_CLIENT_TYPE_Repeater': ['mean'],\n",
       " 'NAME_CLIENT_TYPE_XNA': ['mean'],\n",
       " 'NAME_CLIENT_TYPE_nan': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Additional Service': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Animals': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Audio/Video': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Auto Accessories': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Clothing and Accessories': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Computers': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Construction Materials': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Consumer Electronics': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Direct Sales': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Education': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Fitness': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Furniture': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Gardening': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Homewares': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_House Construction': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Insurance': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Jewelry': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Medical Supplies': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Medicine': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Mobile': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Office Appliances': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Other': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Photo / Cinema Equipment': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Sport and Leisure': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Tourism': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Vehicles': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_Weapon': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_XNA': ['mean'],\n",
       " 'NAME_GOODS_CATEGORY_nan': ['mean'],\n",
       " 'NAME_PORTFOLIO_Cards': ['mean'],\n",
       " 'NAME_PORTFOLIO_Cars': ['mean'],\n",
       " 'NAME_PORTFOLIO_Cash': ['mean'],\n",
       " 'NAME_PORTFOLIO_POS': ['mean'],\n",
       " 'NAME_PORTFOLIO_XNA': ['mean'],\n",
       " 'NAME_PORTFOLIO_nan': ['mean'],\n",
       " 'NAME_PRODUCT_TYPE_XNA': ['mean'],\n",
       " 'NAME_PRODUCT_TYPE_walk-in': ['mean'],\n",
       " 'NAME_PRODUCT_TYPE_x-sell': ['mean'],\n",
       " 'NAME_PRODUCT_TYPE_nan': ['mean'],\n",
       " 'CHANNEL_TYPE_AP+ (Cash loan)': ['mean'],\n",
       " 'CHANNEL_TYPE_Car dealer': ['mean'],\n",
       " 'CHANNEL_TYPE_Channel of corporate sales': ['mean'],\n",
       " 'CHANNEL_TYPE_Contact center': ['mean'],\n",
       " 'CHANNEL_TYPE_Country-wide': ['mean'],\n",
       " 'CHANNEL_TYPE_Credit and cash offices': ['mean'],\n",
       " 'CHANNEL_TYPE_Regional / Local': ['mean'],\n",
       " 'CHANNEL_TYPE_Stone': ['mean'],\n",
       " 'CHANNEL_TYPE_nan': ['mean'],\n",
       " 'NAME_SELLER_INDUSTRY_Auto technology': ['mean'],\n",
       " 'NAME_SELLER_INDUSTRY_Clothing': ['mean'],\n",
       " 'NAME_SELLER_INDUSTRY_Connectivity': ['mean'],\n",
       " 'NAME_SELLER_INDUSTRY_Construction': ['mean'],\n",
       " 'NAME_SELLER_INDUSTRY_Consumer electronics': ['mean'],\n",
       " 'NAME_SELLER_INDUSTRY_Furniture': ['mean'],\n",
       " 'NAME_SELLER_INDUSTRY_Industry': ['mean'],\n",
       " 'NAME_SELLER_INDUSTRY_Jewelry': ['mean'],\n",
       " 'NAME_SELLER_INDUSTRY_MLM partners': ['mean'],\n",
       " 'NAME_SELLER_INDUSTRY_Tourism': ['mean'],\n",
       " 'NAME_SELLER_INDUSTRY_XNA': ['mean'],\n",
       " 'NAME_SELLER_INDUSTRY_nan': ['mean'],\n",
       " 'NAME_YIELD_GROUP_XNA': ['mean'],\n",
       " 'NAME_YIELD_GROUP_high': ['mean'],\n",
       " 'NAME_YIELD_GROUP_low_action': ['mean'],\n",
       " 'NAME_YIELD_GROUP_low_normal': ['mean'],\n",
       " 'NAME_YIELD_GROUP_middle': ['mean'],\n",
       " 'NAME_YIELD_GROUP_nan': ['mean'],\n",
       " 'PRODUCT_COMBINATION_Card Street': ['mean'],\n",
       " 'PRODUCT_COMBINATION_Card X-Sell': ['mean'],\n",
       " 'PRODUCT_COMBINATION_Cash': ['mean'],\n",
       " 'PRODUCT_COMBINATION_Cash Street: high': ['mean'],\n",
       " 'PRODUCT_COMBINATION_Cash Street: low': ['mean'],\n",
       " 'PRODUCT_COMBINATION_Cash Street: middle': ['mean'],\n",
       " 'PRODUCT_COMBINATION_Cash X-Sell: high': ['mean'],\n",
       " 'PRODUCT_COMBINATION_Cash X-Sell: low': ['mean'],\n",
       " 'PRODUCT_COMBINATION_Cash X-Sell: middle': ['mean'],\n",
       " 'PRODUCT_COMBINATION_POS household with interest': ['mean'],\n",
       " 'PRODUCT_COMBINATION_POS household without interest': ['mean'],\n",
       " 'PRODUCT_COMBINATION_POS industry with interest': ['mean'],\n",
       " 'PRODUCT_COMBINATION_POS industry without interest': ['mean'],\n",
       " 'PRODUCT_COMBINATION_POS mobile with interest': ['mean'],\n",
       " 'PRODUCT_COMBINATION_POS mobile without interest': ['mean'],\n",
       " 'PRODUCT_COMBINATION_POS other with interest': ['mean'],\n",
       " 'PRODUCT_COMBINATION_POS others without interest': ['mean'],\n",
       " 'PRODUCT_COMBINATION_nan': ['mean']}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "_uuid": "c1b17ef332116bb87061587cf09a520ada0a5d1e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "159"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cat_aggregations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "_uuid": "744c2a540f03faca03300e610165958b74c53dfa"
   },
   "outputs": [],
   "source": [
    "prev_agg = prev.groupby('SK_ID_CURR').agg({**num_aggregations, **cat_aggregations})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "_uuid": "eeb014c7a7f568b52473b017f0d752ba2bc905fb"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">AMT_ANNUITY</th>\n",
       "      <th colspan=\"3\" halign=\"left\">AMT_APPLICATION</th>\n",
       "      <th colspan=\"3\" halign=\"left\">AMT_CREDIT</th>\n",
       "      <th>APP_CREDIT_PERC</th>\n",
       "      <th>...</th>\n",
       "      <th>PRODUCT_COMBINATION_Cash X-Sell: middle</th>\n",
       "      <th>PRODUCT_COMBINATION_POS household with interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS household without interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS industry with interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS industry without interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS mobile with interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS mobile without interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS other with interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS others without interest</th>\n",
       "      <th>PRODUCT_COMBINATION_nan</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>...</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</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>3951.000</td>\n",
       "      <td>3951.000</td>\n",
       "      <td>3951.000</td>\n",
       "      <td>24835.5</td>\n",
       "      <td>24835.5</td>\n",
       "      <td>24835.50</td>\n",
       "      <td>23787.0</td>\n",
       "      <td>23787.0</td>\n",
       "      <td>23787.00</td>\n",
       "      <td>1.044079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>100002</th>\n",
       "      <td>9251.775</td>\n",
       "      <td>9251.775</td>\n",
       "      <td>9251.775</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.00</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>6737.310</td>\n",
       "      <td>98356.995</td>\n",
       "      <td>56553.990</td>\n",
       "      <td>68809.5</td>\n",
       "      <td>900000.0</td>\n",
       "      <td>435436.50</td>\n",
       "      <td>68053.5</td>\n",
       "      <td>1035882.0</td>\n",
       "      <td>484191.00</td>\n",
       "      <td>0.868825</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</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>100004</th>\n",
       "      <td>5357.250</td>\n",
       "      <td>5357.250</td>\n",
       "      <td>5357.250</td>\n",
       "      <td>24282.0</td>\n",
       "      <td>24282.0</td>\n",
       "      <td>24282.00</td>\n",
       "      <td>20106.0</td>\n",
       "      <td>20106.0</td>\n",
       "      <td>20106.00</td>\n",
       "      <td>1.207699</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>4813.200</td>\n",
       "      <td>4813.200</td>\n",
       "      <td>4813.200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44617.5</td>\n",
       "      <td>22308.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40153.5</td>\n",
       "      <td>20076.75</td>\n",
       "      <td>1.111173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 189 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           AMT_ANNUITY                       AMT_APPLICATION            \\\n",
       "                   min        max       mean             min       max   \n",
       "SK_ID_CURR                                                               \n",
       "100001        3951.000   3951.000   3951.000         24835.5   24835.5   \n",
       "100002        9251.775   9251.775   9251.775        179055.0  179055.0   \n",
       "100003        6737.310  98356.995  56553.990         68809.5  900000.0   \n",
       "100004        5357.250   5357.250   5357.250         24282.0   24282.0   \n",
       "100005        4813.200   4813.200   4813.200             0.0   44617.5   \n",
       "\n",
       "                      AMT_CREDIT                       APP_CREDIT_PERC  \\\n",
       "                 mean        min        max       mean             min   \n",
       "SK_ID_CURR                                                               \n",
       "100001       24835.50    23787.0    23787.0   23787.00        1.044079   \n",
       "100002      179055.00   179055.0   179055.0  179055.00        1.000000   \n",
       "100003      435436.50    68053.5  1035882.0  484191.00        0.868825   \n",
       "100004       24282.00    20106.0    20106.0   20106.00        1.207699   \n",
       "100005       22308.75        0.0    40153.5   20076.75        1.111173   \n",
       "\n",
       "                     ...           PRODUCT_COMBINATION_Cash X-Sell: middle  \\\n",
       "                     ...                                              mean   \n",
       "SK_ID_CURR           ...                                                     \n",
       "100001               ...                                               0.0   \n",
       "100002               ...                                               0.0   \n",
       "100003               ...                                               0.0   \n",
       "100004               ...                                               0.0   \n",
       "100005               ...                                               0.0   \n",
       "\n",
       "           PRODUCT_COMBINATION_POS household with interest  \\\n",
       "                                                      mean   \n",
       "SK_ID_CURR                                                   \n",
       "100001                                            0.000000   \n",
       "100002                                            0.000000   \n",
       "100003                                            0.333333   \n",
       "100004                                            0.000000   \n",
       "100005                                            0.000000   \n",
       "\n",
       "           PRODUCT_COMBINATION_POS household without interest  \\\n",
       "                                                         mean   \n",
       "SK_ID_CURR                                                      \n",
       "100001                                                    0.0   \n",
       "100002                                                    0.0   \n",
       "100003                                                    0.0   \n",
       "100004                                                    0.0   \n",
       "100005                                                    0.0   \n",
       "\n",
       "           PRODUCT_COMBINATION_POS industry with interest  \\\n",
       "                                                     mean   \n",
       "SK_ID_CURR                                                  \n",
       "100001                                           0.000000   \n",
       "100002                                           0.000000   \n",
       "100003                                           0.333333   \n",
       "100004                                           0.000000   \n",
       "100005                                           0.000000   \n",
       "\n",
       "           PRODUCT_COMBINATION_POS industry without interest  \\\n",
       "                                                        mean   \n",
       "SK_ID_CURR                                                     \n",
       "100001                                                   0.0   \n",
       "100002                                                   0.0   \n",
       "100003                                                   0.0   \n",
       "100004                                                   0.0   \n",
       "100005                                                   0.0   \n",
       "\n",
       "           PRODUCT_COMBINATION_POS mobile with interest  \\\n",
       "                                                   mean   \n",
       "SK_ID_CURR                                                \n",
       "100001                                              1.0   \n",
       "100002                                              0.0   \n",
       "100003                                              0.0   \n",
       "100004                                              0.0   \n",
       "100005                                              0.5   \n",
       "\n",
       "           PRODUCT_COMBINATION_POS mobile without interest  \\\n",
       "                                                      mean   \n",
       "SK_ID_CURR                                                   \n",
       "100001                                                 0.0   \n",
       "100002                                                 0.0   \n",
       "100003                                                 0.0   \n",
       "100004                                                 1.0   \n",
       "100005                                                 0.0   \n",
       "\n",
       "           PRODUCT_COMBINATION_POS other with interest  \\\n",
       "                                                  mean   \n",
       "SK_ID_CURR                                               \n",
       "100001                                             0.0   \n",
       "100002                                             1.0   \n",
       "100003                                             0.0   \n",
       "100004                                             0.0   \n",
       "100005                                             0.0   \n",
       "\n",
       "           PRODUCT_COMBINATION_POS others without interest  \\\n",
       "                                                      mean   \n",
       "SK_ID_CURR                                                   \n",
       "100001                                                 0.0   \n",
       "100002                                                 0.0   \n",
       "100003                                                 0.0   \n",
       "100004                                                 0.0   \n",
       "100005                                                 0.0   \n",
       "\n",
       "           PRODUCT_COMBINATION_nan  \n",
       "                              mean  \n",
       "SK_ID_CURR                          \n",
       "100001                         0.0  \n",
       "100002                         0.0  \n",
       "100003                         0.0  \n",
       "100004                         0.0  \n",
       "100005                         0.0  \n",
       "\n",
       "[5 rows x 189 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_uuid": "62f430b9ddb9b14a33f51f75d99d2ddda26034cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(338857, 189)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "_uuid": "6a4eb0c54d4ef0222600804c9fb3977802a90687"
   },
   "outputs": [],
   "source": [
    "prev_agg.columns = pd.Index(['PREV_' + e[0] + \"_\" + e[1].upper() for e in prev_agg.columns.tolist()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_uuid": "adca5af3b7b4751f6fa3851d5881f159bc1c8c88"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(338857, 189)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_uuid": "884143125e3adb6466c0c4d76d852ad9334624dd"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PREV_AMT_ANNUITY_MIN</th>\n",
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       "      <th>PREV_PRODUCT_COMBINATION_POS household with interest_MEAN</th>\n",
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       "      <th>PREV_PRODUCT_COMBINATION_POS other with interest_MEAN</th>\n",
       "      <th>PREV_PRODUCT_COMBINATION_POS others without interest_MEAN</th>\n",
       "      <th>PREV_PRODUCT_COMBINATION_nan_MEAN</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>3951.000</td>\n",
       "      <td>3951.000</td>\n",
       "      <td>3951.000</td>\n",
       "      <td>24835.5</td>\n",
       "      <td>24835.5</td>\n",
       "      <td>24835.50</td>\n",
       "      <td>23787.0</td>\n",
       "      <td>23787.0</td>\n",
       "      <td>23787.00</td>\n",
       "      <td>1.044079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>100002</th>\n",
       "      <td>9251.775</td>\n",
       "      <td>9251.775</td>\n",
       "      <td>9251.775</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.00</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>6737.310</td>\n",
       "      <td>98356.995</td>\n",
       "      <td>56553.990</td>\n",
       "      <td>68809.5</td>\n",
       "      <td>900000.0</td>\n",
       "      <td>435436.50</td>\n",
       "      <td>68053.5</td>\n",
       "      <td>1035882.0</td>\n",
       "      <td>484191.00</td>\n",
       "      <td>0.868825</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</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>100004</th>\n",
       "      <td>5357.250</td>\n",
       "      <td>5357.250</td>\n",
       "      <td>5357.250</td>\n",
       "      <td>24282.0</td>\n",
       "      <td>24282.0</td>\n",
       "      <td>24282.00</td>\n",
       "      <td>20106.0</td>\n",
       "      <td>20106.0</td>\n",
       "      <td>20106.00</td>\n",
       "      <td>1.207699</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>4813.200</td>\n",
       "      <td>4813.200</td>\n",
       "      <td>4813.200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44617.5</td>\n",
       "      <td>22308.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40153.5</td>\n",
       "      <td>20076.75</td>\n",
       "      <td>1.111173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 189 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PREV_AMT_ANNUITY_MIN  PREV_AMT_ANNUITY_MAX  PREV_AMT_ANNUITY_MEAN  \\\n",
       "SK_ID_CURR                                                                      \n",
       "100001                  3951.000              3951.000               3951.000   \n",
       "100002                  9251.775              9251.775               9251.775   \n",
       "100003                  6737.310             98356.995              56553.990   \n",
       "100004                  5357.250              5357.250               5357.250   \n",
       "100005                  4813.200              4813.200               4813.200   \n",
       "\n",
       "            PREV_AMT_APPLICATION_MIN  PREV_AMT_APPLICATION_MAX  \\\n",
       "SK_ID_CURR                                                       \n",
       "100001                       24835.5                   24835.5   \n",
       "100002                      179055.0                  179055.0   \n",
       "100003                       68809.5                  900000.0   \n",
       "100004                       24282.0                   24282.0   \n",
       "100005                           0.0                   44617.5   \n",
       "\n",
       "            PREV_AMT_APPLICATION_MEAN  PREV_AMT_CREDIT_MIN  \\\n",
       "SK_ID_CURR                                                   \n",
       "100001                       24835.50              23787.0   \n",
       "100002                      179055.00             179055.0   \n",
       "100003                      435436.50              68053.5   \n",
       "100004                       24282.00              20106.0   \n",
       "100005                       22308.75                  0.0   \n",
       "\n",
       "            PREV_AMT_CREDIT_MAX  PREV_AMT_CREDIT_MEAN  \\\n",
       "SK_ID_CURR                                              \n",
       "100001                  23787.0              23787.00   \n",
       "100002                 179055.0             179055.00   \n",
       "100003                1035882.0             484191.00   \n",
       "100004                  20106.0              20106.00   \n",
       "100005                  40153.5              20076.75   \n",
       "\n",
       "            PREV_APP_CREDIT_PERC_MIN                ...                  \\\n",
       "SK_ID_CURR                                          ...                   \n",
       "100001                      1.044079                ...                   \n",
       "100002                      1.000000                ...                   \n",
       "100003                      0.868825                ...                   \n",
       "100004                      1.207699                ...                   \n",
       "100005                      1.111173                ...                   \n",
       "\n",
       "            PREV_PRODUCT_COMBINATION_Cash X-Sell: middle_MEAN  \\\n",
       "SK_ID_CURR                                                      \n",
       "100001                                                    0.0   \n",
       "100002                                                    0.0   \n",
       "100003                                                    0.0   \n",
       "100004                                                    0.0   \n",
       "100005                                                    0.0   \n",
       "\n",
       "            PREV_PRODUCT_COMBINATION_POS household with interest_MEAN  \\\n",
       "SK_ID_CURR                                                              \n",
       "100001                                               0.000000           \n",
       "100002                                               0.000000           \n",
       "100003                                               0.333333           \n",
       "100004                                               0.000000           \n",
       "100005                                               0.000000           \n",
       "\n",
       "            PREV_PRODUCT_COMBINATION_POS household without interest_MEAN  \\\n",
       "SK_ID_CURR                                                                 \n",
       "100001                                                    0.0              \n",
       "100002                                                    0.0              \n",
       "100003                                                    0.0              \n",
       "100004                                                    0.0              \n",
       "100005                                                    0.0              \n",
       "\n",
       "            PREV_PRODUCT_COMBINATION_POS industry with interest_MEAN  \\\n",
       "SK_ID_CURR                                                             \n",
       "100001                                               0.000000          \n",
       "100002                                               0.000000          \n",
       "100003                                               0.333333          \n",
       "100004                                               0.000000          \n",
       "100005                                               0.000000          \n",
       "\n",
       "            PREV_PRODUCT_COMBINATION_POS industry without interest_MEAN  \\\n",
       "SK_ID_CURR                                                                \n",
       "100001                                                    0.0             \n",
       "100002                                                    0.0             \n",
       "100003                                                    0.0             \n",
       "100004                                                    0.0             \n",
       "100005                                                    0.0             \n",
       "\n",
       "            PREV_PRODUCT_COMBINATION_POS mobile with interest_MEAN  \\\n",
       "SK_ID_CURR                                                           \n",
       "100001                                                    1.0        \n",
       "100002                                                    0.0        \n",
       "100003                                                    0.0        \n",
       "100004                                                    0.0        \n",
       "100005                                                    0.5        \n",
       "\n",
       "            PREV_PRODUCT_COMBINATION_POS mobile without interest_MEAN  \\\n",
       "SK_ID_CURR                                                              \n",
       "100001                                                    0.0           \n",
       "100002                                                    0.0           \n",
       "100003                                                    0.0           \n",
       "100004                                                    1.0           \n",
       "100005                                                    0.0           \n",
       "\n",
       "            PREV_PRODUCT_COMBINATION_POS other with interest_MEAN  \\\n",
       "SK_ID_CURR                                                          \n",
       "100001                                                    0.0       \n",
       "100002                                                    1.0       \n",
       "100003                                                    0.0       \n",
       "100004                                                    0.0       \n",
       "100005                                                    0.0       \n",
       "\n",
       "            PREV_PRODUCT_COMBINATION_POS others without interest_MEAN  \\\n",
       "SK_ID_CURR                                                              \n",
       "100001                                                    0.0           \n",
       "100002                                                    0.0           \n",
       "100003                                                    0.0           \n",
       "100004                                                    0.0           \n",
       "100005                                                    0.0           \n",
       "\n",
       "            PREV_PRODUCT_COMBINATION_nan_MEAN  \n",
       "SK_ID_CURR                                     \n",
       "100001                                    0.0  \n",
       "100002                                    0.0  \n",
       "100003                                    0.0  \n",
       "100004                                    0.0  \n",
       "100005                                    0.0  \n",
       "\n",
       "[5 rows x 189 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "_uuid": "319ebd798a0d59253380da47e9a9c8c3e3a7d754"
   },
   "outputs": [],
   "source": [
    "# Previous Applications: Approved Applications - only numerical features\n",
    "approved = prev[prev['NAME_CONTRACT_STATUS_Approved'] == 1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "_uuid": "60afe85e659de732eff25f02ebd3aebe12402ee2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1036781, 181)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approved.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "_uuid": "401aa8086b1340a977a1d0b8fe566d34201f91ea"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_PREV</th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_APPLICATION</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_DOWN_PAYMENT</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <th>NFLAG_LAST_APPL_IN_DAY</th>\n",
       "      <th>RATE_DOWN_PAYMENT</th>\n",
       "      <th>...</th>\n",
       "      <th>PRODUCT_COMBINATION_POS household with interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS household without interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS industry with interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS industry without interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS mobile with interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS mobile without interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS other with interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS others without interest</th>\n",
       "      <th>PRODUCT_COMBINATION_nan</th>\n",
       "      <th>APP_CREDIT_PERC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2030495</td>\n",
       "      <td>271877</td>\n",
       "      <td>1730.430</td>\n",
       "      <td>17145.0</td>\n",
       "      <td>17145.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17145.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2802425</td>\n",
       "      <td>108129</td>\n",
       "      <td>25188.615</td>\n",
       "      <td>607500.0</td>\n",
       "      <td>679671.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>607500.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.893815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2523466</td>\n",
       "      <td>122040</td>\n",
       "      <td>15060.735</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>136444.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.824511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2819243</td>\n",
       "      <td>176158</td>\n",
       "      <td>47041.335</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>470790.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.955840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1383531</td>\n",
       "      <td>199383</td>\n",
       "      <td>23703.930</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>340573.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.924910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 181 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_PREV  SK_ID_CURR  AMT_ANNUITY  AMT_APPLICATION  AMT_CREDIT  \\\n",
       "0     2030495      271877     1730.430          17145.0     17145.0   \n",
       "1     2802425      108129    25188.615         607500.0    679671.0   \n",
       "2     2523466      122040    15060.735         112500.0    136444.5   \n",
       "3     2819243      176158    47041.335         450000.0    470790.0   \n",
       "5     1383531      199383    23703.930         315000.0    340573.5   \n",
       "\n",
       "   AMT_DOWN_PAYMENT  AMT_GOODS_PRICE  HOUR_APPR_PROCESS_START  \\\n",
       "0               0.0          17145.0                       15   \n",
       "1               NaN         607500.0                       11   \n",
       "2               NaN         112500.0                       11   \n",
       "3               NaN         450000.0                        7   \n",
       "5               NaN         315000.0                        8   \n",
       "\n",
       "   NFLAG_LAST_APPL_IN_DAY  RATE_DOWN_PAYMENT       ...         \\\n",
       "0                       1                0.0       ...          \n",
       "1                       1                NaN       ...          \n",
       "2                       1                NaN       ...          \n",
       "3                       1                NaN       ...          \n",
       "5                       1                NaN       ...          \n",
       "\n",
       "   PRODUCT_COMBINATION_POS household with interest  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "5                                                0   \n",
       "\n",
       "   PRODUCT_COMBINATION_POS household without interest  \\\n",
       "0                                                  0    \n",
       "1                                                  0    \n",
       "2                                                  0    \n",
       "3                                                  0    \n",
       "5                                                  0    \n",
       "\n",
       "   PRODUCT_COMBINATION_POS industry with interest  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "5                                               0   \n",
       "\n",
       "   PRODUCT_COMBINATION_POS industry without interest  \\\n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "5                                                  0   \n",
       "\n",
       "   PRODUCT_COMBINATION_POS mobile with interest  \\\n",
       "0                                             1   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "5                                             0   \n",
       "\n",
       "   PRODUCT_COMBINATION_POS mobile without interest  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "5                                                0   \n",
       "\n",
       "   PRODUCT_COMBINATION_POS other with interest  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "5                                            0   \n",
       "\n",
       "   PRODUCT_COMBINATION_POS others without interest  PRODUCT_COMBINATION_nan  \\\n",
       "0                                                0                        0   \n",
       "1                                                0                        0   \n",
       "2                                                0                        0   \n",
       "3                                                0                        0   \n",
       "5                                                0                        0   \n",
       "\n",
       "   APP_CREDIT_PERC  \n",
       "0         1.000000  \n",
       "1         0.893815  \n",
       "2         0.824511  \n",
       "3         0.955840  \n",
       "5         0.924910  \n",
       "\n",
       "[5 rows x 181 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approved.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_uuid": "416446debc805892dbb96e35454abe1fc1b520d3"
   },
   "outputs": [],
   "source": [
    "approved_agg = approved.groupby('SK_ID_CURR').agg(num_aggregations)\n",
    "approved_agg.columns = pd.Index(['APPROVED_' + e[0] + \"_\" + e[1].upper() for e in approved_agg.columns.tolist()])\n",
    "prev_agg = prev_agg.join(approved_agg, how='left', on='SK_ID_CURR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "_uuid": "81b91fcc57012aae760bd31bc07b6ef04dc3851d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(338857, 219)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "_uuid": "ffd3dfce0b081ce6b8e0b9a80951b9ed0d6ea106"
   },
   "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>PREV_AMT_ANNUITY_MIN</th>\n",
       "      <th>PREV_AMT_ANNUITY_MAX</th>\n",
       "      <th>PREV_AMT_ANNUITY_MEAN</th>\n",
       "      <th>PREV_AMT_APPLICATION_MIN</th>\n",
       "      <th>PREV_AMT_APPLICATION_MAX</th>\n",
       "      <th>PREV_AMT_APPLICATION_MEAN</th>\n",
       "      <th>PREV_AMT_CREDIT_MIN</th>\n",
       "      <th>PREV_AMT_CREDIT_MAX</th>\n",
       "      <th>PREV_AMT_CREDIT_MEAN</th>\n",
       "      <th>PREV_APP_CREDIT_PERC_MIN</th>\n",
       "      <th>...</th>\n",
       "      <th>APPROVED_HOUR_APPR_PROCESS_START_MAX</th>\n",
       "      <th>APPROVED_HOUR_APPR_PROCESS_START_MEAN</th>\n",
       "      <th>APPROVED_RATE_DOWN_PAYMENT_MIN</th>\n",
       "      <th>APPROVED_RATE_DOWN_PAYMENT_MAX</th>\n",
       "      <th>APPROVED_RATE_DOWN_PAYMENT_MEAN</th>\n",
       "      <th>APPROVED_DAYS_DECISION_MIN</th>\n",
       "      <th>APPROVED_DAYS_DECISION_MAX</th>\n",
       "      <th>APPROVED_DAYS_DECISION_MEAN</th>\n",
       "      <th>APPROVED_CNT_PAYMENT_MEAN</th>\n",
       "      <th>APPROVED_CNT_PAYMENT_SUM</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>3951.000</td>\n",
       "      <td>3951.000</td>\n",
       "      <td>3951.000</td>\n",
       "      <td>24835.5</td>\n",
       "      <td>24835.5</td>\n",
       "      <td>24835.50</td>\n",
       "      <td>23787.0</td>\n",
       "      <td>23787.0</td>\n",
       "      <td>23787.00</td>\n",
       "      <td>1.044079</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.104326</td>\n",
       "      <td>0.104326</td>\n",
       "      <td>0.104326</td>\n",
       "      <td>-1740.0</td>\n",
       "      <td>-1740.0</td>\n",
       "      <td>-1740.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>9251.775</td>\n",
       "      <td>9251.775</td>\n",
       "      <td>9251.775</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.00</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-606.0</td>\n",
       "      <td>-606.0</td>\n",
       "      <td>-606.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>6737.310</td>\n",
       "      <td>98356.995</td>\n",
       "      <td>56553.990</td>\n",
       "      <td>68809.5</td>\n",
       "      <td>900000.0</td>\n",
       "      <td>435436.50</td>\n",
       "      <td>68053.5</td>\n",
       "      <td>1035882.0</td>\n",
       "      <td>484191.00</td>\n",
       "      <td>0.868825</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100061</td>\n",
       "      <td>0.050030</td>\n",
       "      <td>-2341.0</td>\n",
       "      <td>-746.0</td>\n",
       "      <td>-1305.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>5357.250</td>\n",
       "      <td>5357.250</td>\n",
       "      <td>5357.250</td>\n",
       "      <td>24282.0</td>\n",
       "      <td>24282.0</td>\n",
       "      <td>24282.00</td>\n",
       "      <td>20106.0</td>\n",
       "      <td>20106.0</td>\n",
       "      <td>20106.00</td>\n",
       "      <td>1.207699</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.212008</td>\n",
       "      <td>0.212008</td>\n",
       "      <td>0.212008</td>\n",
       "      <td>-815.0</td>\n",
       "      <td>-815.0</td>\n",
       "      <td>-815.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>4813.200</td>\n",
       "      <td>4813.200</td>\n",
       "      <td>4813.200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44617.5</td>\n",
       "      <td>22308.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40153.5</td>\n",
       "      <td>20076.75</td>\n",
       "      <td>1.111173</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.108964</td>\n",
       "      <td>0.108964</td>\n",
       "      <td>0.108964</td>\n",
       "      <td>-757.0</td>\n",
       "      <td>-757.0</td>\n",
       "      <td>-757.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 219 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PREV_AMT_ANNUITY_MIN  PREV_AMT_ANNUITY_MAX  PREV_AMT_ANNUITY_MEAN  \\\n",
       "SK_ID_CURR                                                                      \n",
       "100001                  3951.000              3951.000               3951.000   \n",
       "100002                  9251.775              9251.775               9251.775   \n",
       "100003                  6737.310             98356.995              56553.990   \n",
       "100004                  5357.250              5357.250               5357.250   \n",
       "100005                  4813.200              4813.200               4813.200   \n",
       "\n",
       "            PREV_AMT_APPLICATION_MIN  PREV_AMT_APPLICATION_MAX  \\\n",
       "SK_ID_CURR                                                       \n",
       "100001                       24835.5                   24835.5   \n",
       "100002                      179055.0                  179055.0   \n",
       "100003                       68809.5                  900000.0   \n",
       "100004                       24282.0                   24282.0   \n",
       "100005                           0.0                   44617.5   \n",
       "\n",
       "            PREV_AMT_APPLICATION_MEAN  PREV_AMT_CREDIT_MIN  \\\n",
       "SK_ID_CURR                                                   \n",
       "100001                       24835.50              23787.0   \n",
       "100002                      179055.00             179055.0   \n",
       "100003                      435436.50              68053.5   \n",
       "100004                       24282.00              20106.0   \n",
       "100005                       22308.75                  0.0   \n",
       "\n",
       "            PREV_AMT_CREDIT_MAX  PREV_AMT_CREDIT_MEAN  \\\n",
       "SK_ID_CURR                                              \n",
       "100001                  23787.0              23787.00   \n",
       "100002                 179055.0             179055.00   \n",
       "100003                1035882.0             484191.00   \n",
       "100004                  20106.0              20106.00   \n",
       "100005                  40153.5              20076.75   \n",
       "\n",
       "            PREV_APP_CREDIT_PERC_MIN            ...             \\\n",
       "SK_ID_CURR                                      ...              \n",
       "100001                      1.044079            ...              \n",
       "100002                      1.000000            ...              \n",
       "100003                      0.868825            ...              \n",
       "100004                      1.207699            ...              \n",
       "100005                      1.111173            ...              \n",
       "\n",
       "            APPROVED_HOUR_APPR_PROCESS_START_MAX  \\\n",
       "SK_ID_CURR                                         \n",
       "100001                                      13.0   \n",
       "100002                                       9.0   \n",
       "100003                                      17.0   \n",
       "100004                                       5.0   \n",
       "100005                                      11.0   \n",
       "\n",
       "            APPROVED_HOUR_APPR_PROCESS_START_MEAN  \\\n",
       "SK_ID_CURR                                          \n",
       "100001                                  13.000000   \n",
       "100002                                   9.000000   \n",
       "100003                                  14.666667   \n",
       "100004                                   5.000000   \n",
       "100005                                  11.000000   \n",
       "\n",
       "            APPROVED_RATE_DOWN_PAYMENT_MIN  APPROVED_RATE_DOWN_PAYMENT_MAX  \\\n",
       "SK_ID_CURR                                                                   \n",
       "100001                            0.104326                        0.104326   \n",
       "100002                            0.000000                        0.000000   \n",
       "100003                            0.000000                        0.100061   \n",
       "100004                            0.212008                        0.212008   \n",
       "100005                            0.108964                        0.108964   \n",
       "\n",
       "            APPROVED_RATE_DOWN_PAYMENT_MEAN  APPROVED_DAYS_DECISION_MIN  \\\n",
       "SK_ID_CURR                                                                \n",
       "100001                             0.104326                     -1740.0   \n",
       "100002                             0.000000                      -606.0   \n",
       "100003                             0.050030                     -2341.0   \n",
       "100004                             0.212008                      -815.0   \n",
       "100005                             0.108964                      -757.0   \n",
       "\n",
       "            APPROVED_DAYS_DECISION_MAX  APPROVED_DAYS_DECISION_MEAN  \\\n",
       "SK_ID_CURR                                                            \n",
       "100001                         -1740.0                      -1740.0   \n",
       "100002                          -606.0                       -606.0   \n",
       "100003                          -746.0                      -1305.0   \n",
       "100004                          -815.0                       -815.0   \n",
       "100005                          -757.0                       -757.0   \n",
       "\n",
       "            APPROVED_CNT_PAYMENT_MEAN  APPROVED_CNT_PAYMENT_SUM  \n",
       "SK_ID_CURR                                                       \n",
       "100001                            8.0                       8.0  \n",
       "100002                           24.0                      24.0  \n",
       "100003                           10.0                      30.0  \n",
       "100004                            4.0                       4.0  \n",
       "100005                           12.0                      12.0  \n",
       "\n",
       "[5 rows x 219 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "_uuid": "9108360fb695dabcfc27e41f76f470e5367432f4"
   },
   "outputs": [],
   "source": [
    "# Previous Applications: Refused Applications - only numerical features\n",
    "refused = prev[prev['NAME_CONTRACT_STATUS_Refused'] == 1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "_uuid": "612c7582b9df5f77bf13defb38aced6c5b8a7896"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_PREV</th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_APPLICATION</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_DOWN_PAYMENT</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <th>NFLAG_LAST_APPL_IN_DAY</th>\n",
       "      <th>RATE_DOWN_PAYMENT</th>\n",
       "      <th>...</th>\n",
       "      <th>PRODUCT_COMBINATION_POS household with interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS household without interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS industry with interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS industry without interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS mobile with interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS mobile without interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS other with interest</th>\n",
       "      <th>PRODUCT_COMBINATION_POS others without interest</th>\n",
       "      <th>PRODUCT_COMBINATION_nan</th>\n",
       "      <th>APP_CREDIT_PERC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1784265</td>\n",
       "      <td>202054</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1173070</td>\n",
       "      <td>199178</td>\n",
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       "      <td>45000.0</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1506815</td>\n",
       "      <td>166490</td>\n",
       "      <td>25454.025</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>491580.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.915416</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1172842</td>\n",
       "      <td>302212</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1172937</td>\n",
       "      <td>302212</td>\n",
       "      <td>39475.305</td>\n",
       "      <td>1129500.0</td>\n",
       "      <td>1277104.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1129500.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.884423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 181 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SK_ID_PREV  SK_ID_CURR  AMT_ANNUITY  AMT_APPLICATION  AMT_CREDIT  \\\n",
       "4      1784265      202054    31924.395         337500.0    404055.0   \n",
       "19     1173070      199178     4666.500          45000.0     49455.0   \n",
       "20     1506815      166490    25454.025         450000.0    491580.0   \n",
       "22     1172842      302212          NaN              0.0         0.0   \n",
       "23     1172937      302212    39475.305        1129500.0   1277104.5   \n",
       "\n",
       "    AMT_DOWN_PAYMENT  AMT_GOODS_PRICE  HOUR_APPR_PROCESS_START  \\\n",
       "4                NaN         337500.0                        9   \n",
       "19               NaN          45000.0                       16   \n",
       "20               NaN         450000.0                        6   \n",
       "22               NaN              NaN                        9   \n",
       "23               NaN        1129500.0                        5   \n",
       "\n",
       "    NFLAG_LAST_APPL_IN_DAY  RATE_DOWN_PAYMENT       ...         \\\n",
       "4                        1                NaN       ...          \n",
       "19                       1                NaN       ...          \n",
       "20                       1                NaN       ...          \n",
       "22                       1                NaN       ...          \n",
       "23                       1                NaN       ...          \n",
       "\n",
       "    PRODUCT_COMBINATION_POS household with interest  \\\n",
       "4                                                 0   \n",
       "19                                                0   \n",
       "20                                                0   \n",
       "22                                                0   \n",
       "23                                                0   \n",
       "\n",
       "    PRODUCT_COMBINATION_POS household without interest  \\\n",
       "4                                                   0    \n",
       "19                                                  0    \n",
       "20                                                  0    \n",
       "22                                                  0    \n",
       "23                                                  0    \n",
       "\n",
       "    PRODUCT_COMBINATION_POS industry with interest  \\\n",
       "4                                                0   \n",
       "19                                               0   \n",
       "20                                               0   \n",
       "22                                               0   \n",
       "23                                               0   \n",
       "\n",
       "    PRODUCT_COMBINATION_POS industry without interest  \\\n",
       "4                                                   0   \n",
       "19                                                  0   \n",
       "20                                                  0   \n",
       "22                                                  0   \n",
       "23                                                  0   \n",
       "\n",
       "    PRODUCT_COMBINATION_POS mobile with interest  \\\n",
       "4                                              0   \n",
       "19                                             0   \n",
       "20                                             0   \n",
       "22                                             0   \n",
       "23                                             0   \n",
       "\n",
       "    PRODUCT_COMBINATION_POS mobile without interest  \\\n",
       "4                                                 0   \n",
       "19                                                0   \n",
       "20                                                0   \n",
       "22                                                0   \n",
       "23                                                0   \n",
       "\n",
       "    PRODUCT_COMBINATION_POS other with interest  \\\n",
       "4                                             0   \n",
       "19                                            0   \n",
       "20                                            0   \n",
       "22                                            0   \n",
       "23                                            0   \n",
       "\n",
       "    PRODUCT_COMBINATION_POS others without interest  PRODUCT_COMBINATION_nan  \\\n",
       "4                                                 0                        0   \n",
       "19                                                0                        0   \n",
       "20                                                0                        0   \n",
       "22                                                0                        0   \n",
       "23                                                0                        0   \n",
       "\n",
       "    APP_CREDIT_PERC  \n",
       "4          0.835282  \n",
       "19         0.909918  \n",
       "20         0.915416  \n",
       "22              NaN  \n",
       "23         0.884423  \n",
       "\n",
       "[5 rows x 181 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refused.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "_uuid": "ffd9b85b7214348aed4de587ccbc541bb286a8ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(290678, 181)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refused.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "_uuid": "e26178fdd1e9817bfecbb6cdd1f5f3b541c6e426"
   },
   "outputs": [],
   "source": [
    "refused_agg = refused.groupby('SK_ID_CURR').agg(num_aggregations)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "_uuid": "548a5539e860570f7506b9b3358473047683e7f7"
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">AMT_ANNUITY</th>\n",
       "      <th colspan=\"3\" halign=\"left\">AMT_APPLICATION</th>\n",
       "      <th colspan=\"3\" halign=\"left\">AMT_CREDIT</th>\n",
       "      <th>APP_CREDIT_PERC</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">HOUR_APPR_PROCESS_START</th>\n",
       "      <th colspan=\"3\" halign=\"left\">RATE_DOWN_PAYMENT</th>\n",
       "      <th colspan=\"3\" halign=\"left\">DAYS_DECISION</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CNT_PAYMENT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>...</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</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>100006</th>\n",
       "      <td>32696.100</td>\n",
       "      <td>32696.100</td>\n",
       "      <td>32696.100</td>\n",
       "      <td>688500.000</td>\n",
       "      <td>688500.0</td>\n",
       "      <td>688500.0000</td>\n",
       "      <td>906615.000</td>\n",
       "      <td>906615.0</td>\n",
       "      <td>906615.0000</td>\n",
       "      <td>0.759418</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>15.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-181</td>\n",
       "      <td>-181</td>\n",
       "      <td>-181.000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100011</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>9.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1162</td>\n",
       "      <td>-1162</td>\n",
       "      <td>-1162.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100027</th>\n",
       "      <td>22556.475</td>\n",
       "      <td>22556.475</td>\n",
       "      <td>22556.475</td>\n",
       "      <td>225000.000</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>225000.0000</td>\n",
       "      <td>239850.000</td>\n",
       "      <td>239850.0</td>\n",
       "      <td>239850.0000</td>\n",
       "      <td>0.938086</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>14.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-181</td>\n",
       "      <td>-181</td>\n",
       "      <td>-181.000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100030</th>\n",
       "      <td>2826.450</td>\n",
       "      <td>6176.925</td>\n",
       "      <td>4073.265</td>\n",
       "      <td>21969.225</td>\n",
       "      <td>43870.5</td>\n",
       "      <td>33767.1225</td>\n",
       "      <td>21969.225</td>\n",
       "      <td>43870.5</td>\n",
       "      <td>32533.2225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>12.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.210919</td>\n",
       "      <td>0.042874</td>\n",
       "      <td>-2689</td>\n",
       "      <td>-840</td>\n",
       "      <td>-2053.900</td>\n",
       "      <td>9.833333</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100035</th>\n",
       "      <td>22308.750</td>\n",
       "      <td>33238.800</td>\n",
       "      <td>27773.775</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1260000.0</td>\n",
       "      <td>241875.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1260000.0</td>\n",
       "      <td>241875.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>12.375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-160</td>\n",
       "      <td>-119</td>\n",
       "      <td>-143.375</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           AMT_ANNUITY                       AMT_APPLICATION             \\\n",
       "                   min        max       mean             min        max   \n",
       "SK_ID_CURR                                                                \n",
       "100006       32696.100  32696.100  32696.100      688500.000   688500.0   \n",
       "100011             NaN        NaN        NaN           0.000        0.0   \n",
       "100027       22556.475  22556.475  22556.475      225000.000   225000.0   \n",
       "100030        2826.450   6176.925   4073.265       21969.225    43870.5   \n",
       "100035       22308.750  33238.800  27773.775           0.000  1260000.0   \n",
       "\n",
       "                         AMT_CREDIT                         APP_CREDIT_PERC  \\\n",
       "                   mean         min        max         mean             min   \n",
       "SK_ID_CURR                                                                    \n",
       "100006      688500.0000  906615.000   906615.0  906615.0000        0.759418   \n",
       "100011           0.0000       0.000        0.0       0.0000             NaN   \n",
       "100027      225000.0000  239850.000   239850.0  239850.0000        0.938086   \n",
       "100030       33767.1225   21969.225    43870.5   32533.2225        1.000000   \n",
       "100035      241875.0000       0.000  1260000.0  241875.0000        1.000000   \n",
       "\n",
       "            ...   HOUR_APPR_PROCESS_START         RATE_DOWN_PAYMENT            \\\n",
       "            ...                       max    mean               min       max   \n",
       "SK_ID_CURR  ...                                                                 \n",
       "100006      ...                        15  15.000               NaN       NaN   \n",
       "100011      ...                         9   9.000               NaN       NaN   \n",
       "100027      ...                        14  14.000               NaN       NaN   \n",
       "100030      ...                        16  12.500               0.0  0.210919   \n",
       "100035      ...                        14  12.375               NaN       NaN   \n",
       "\n",
       "                     DAYS_DECISION                 CNT_PAYMENT         \n",
       "                mean           min   max      mean        mean    sum  \n",
       "SK_ID_CURR                                                             \n",
       "100006           NaN          -181  -181  -181.000   48.000000   48.0  \n",
       "100011           NaN         -1162 -1162 -1162.000         NaN    0.0  \n",
       "100027           NaN          -181  -181  -181.000   12.000000   12.0  \n",
       "100030      0.042874         -2689  -840 -2053.900    9.833333   59.0  \n",
       "100035           NaN          -160  -119  -143.375   54.000000  108.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refused_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "_uuid": "345b976d26b7adc79351536153b6803537359037"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118277, 30)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refused_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "_uuid": "9a9ddb683e0e02672769ef46d1421e93c35c53ce"
   },
   "outputs": [],
   "source": [
    "refused_agg.columns = pd.Index(['REFUSED_' + e[0] + \"_\" + e[1].upper() for e in refused_agg.columns.tolist()])\n",
    "prev_agg = prev_agg.join(refused_agg, how='left', on='SK_ID_CURR')\n",
    "del refused, refused_agg, approved, approved_agg, prev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "_uuid": "37ad38ad71cd655bdf2891f7cae2676bf6a7f2a7"
   },
   "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>PREV_AMT_ANNUITY_MIN</th>\n",
       "      <th>PREV_AMT_ANNUITY_MAX</th>\n",
       "      <th>PREV_AMT_ANNUITY_MEAN</th>\n",
       "      <th>PREV_AMT_APPLICATION_MIN</th>\n",
       "      <th>PREV_AMT_APPLICATION_MAX</th>\n",
       "      <th>PREV_AMT_APPLICATION_MEAN</th>\n",
       "      <th>PREV_AMT_CREDIT_MIN</th>\n",
       "      <th>PREV_AMT_CREDIT_MAX</th>\n",
       "      <th>PREV_AMT_CREDIT_MEAN</th>\n",
       "      <th>PREV_APP_CREDIT_PERC_MIN</th>\n",
       "      <th>...</th>\n",
       "      <th>REFUSED_HOUR_APPR_PROCESS_START_MAX</th>\n",
       "      <th>REFUSED_HOUR_APPR_PROCESS_START_MEAN</th>\n",
       "      <th>REFUSED_RATE_DOWN_PAYMENT_MIN</th>\n",
       "      <th>REFUSED_RATE_DOWN_PAYMENT_MAX</th>\n",
       "      <th>REFUSED_RATE_DOWN_PAYMENT_MEAN</th>\n",
       "      <th>REFUSED_DAYS_DECISION_MIN</th>\n",
       "      <th>REFUSED_DAYS_DECISION_MAX</th>\n",
       "      <th>REFUSED_DAYS_DECISION_MEAN</th>\n",
       "      <th>REFUSED_CNT_PAYMENT_MEAN</th>\n",
       "      <th>REFUSED_CNT_PAYMENT_SUM</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>3951.000</td>\n",
       "      <td>3951.000</td>\n",
       "      <td>3951.000</td>\n",
       "      <td>24835.5</td>\n",
       "      <td>24835.5</td>\n",
       "      <td>24835.50</td>\n",
       "      <td>23787.0</td>\n",
       "      <td>23787.0</td>\n",
       "      <td>23787.00</td>\n",
       "      <td>1.044079</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",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>9251.775</td>\n",
       "      <td>9251.775</td>\n",
       "      <td>9251.775</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.00</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.00</td>\n",
       "      <td>1.000000</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",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>6737.310</td>\n",
       "      <td>98356.995</td>\n",
       "      <td>56553.990</td>\n",
       "      <td>68809.5</td>\n",
       "      <td>900000.0</td>\n",
       "      <td>435436.50</td>\n",
       "      <td>68053.5</td>\n",
       "      <td>1035882.0</td>\n",
       "      <td>484191.00</td>\n",
       "      <td>0.868825</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",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>5357.250</td>\n",
       "      <td>5357.250</td>\n",
       "      <td>5357.250</td>\n",
       "      <td>24282.0</td>\n",
       "      <td>24282.0</td>\n",
       "      <td>24282.00</td>\n",
       "      <td>20106.0</td>\n",
       "      <td>20106.0</td>\n",
       "      <td>20106.00</td>\n",
       "      <td>1.207699</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",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>4813.200</td>\n",
       "      <td>4813.200</td>\n",
       "      <td>4813.200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44617.5</td>\n",
       "      <td>22308.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40153.5</td>\n",
       "      <td>20076.75</td>\n",
       "      <td>1.111173</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 × 249 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PREV_AMT_ANNUITY_MIN  PREV_AMT_ANNUITY_MAX  PREV_AMT_ANNUITY_MEAN  \\\n",
       "SK_ID_CURR                                                                      \n",
       "100001                  3951.000              3951.000               3951.000   \n",
       "100002                  9251.775              9251.775               9251.775   \n",
       "100003                  6737.310             98356.995              56553.990   \n",
       "100004                  5357.250              5357.250               5357.250   \n",
       "100005                  4813.200              4813.200               4813.200   \n",
       "\n",
       "            PREV_AMT_APPLICATION_MIN  PREV_AMT_APPLICATION_MAX  \\\n",
       "SK_ID_CURR                                                       \n",
       "100001                       24835.5                   24835.5   \n",
       "100002                      179055.0                  179055.0   \n",
       "100003                       68809.5                  900000.0   \n",
       "100004                       24282.0                   24282.0   \n",
       "100005                           0.0                   44617.5   \n",
       "\n",
       "            PREV_AMT_APPLICATION_MEAN  PREV_AMT_CREDIT_MIN  \\\n",
       "SK_ID_CURR                                                   \n",
       "100001                       24835.50              23787.0   \n",
       "100002                      179055.00             179055.0   \n",
       "100003                      435436.50              68053.5   \n",
       "100004                       24282.00              20106.0   \n",
       "100005                       22308.75                  0.0   \n",
       "\n",
       "            PREV_AMT_CREDIT_MAX  PREV_AMT_CREDIT_MEAN  \\\n",
       "SK_ID_CURR                                              \n",
       "100001                  23787.0              23787.00   \n",
       "100002                 179055.0             179055.00   \n",
       "100003                1035882.0             484191.00   \n",
       "100004                  20106.0              20106.00   \n",
       "100005                  40153.5              20076.75   \n",
       "\n",
       "            PREV_APP_CREDIT_PERC_MIN           ...             \\\n",
       "SK_ID_CURR                                     ...              \n",
       "100001                      1.044079           ...              \n",
       "100002                      1.000000           ...              \n",
       "100003                      0.868825           ...              \n",
       "100004                      1.207699           ...              \n",
       "100005                      1.111173           ...              \n",
       "\n",
       "            REFUSED_HOUR_APPR_PROCESS_START_MAX  \\\n",
       "SK_ID_CURR                                        \n",
       "100001                                      NaN   \n",
       "100002                                      NaN   \n",
       "100003                                      NaN   \n",
       "100004                                      NaN   \n",
       "100005                                      NaN   \n",
       "\n",
       "            REFUSED_HOUR_APPR_PROCESS_START_MEAN  \\\n",
       "SK_ID_CURR                                         \n",
       "100001                                       NaN   \n",
       "100002                                       NaN   \n",
       "100003                                       NaN   \n",
       "100004                                       NaN   \n",
       "100005                                       NaN   \n",
       "\n",
       "            REFUSED_RATE_DOWN_PAYMENT_MIN  REFUSED_RATE_DOWN_PAYMENT_MAX  \\\n",
       "SK_ID_CURR                                                                 \n",
       "100001                                NaN                            NaN   \n",
       "100002                                NaN                            NaN   \n",
       "100003                                NaN                            NaN   \n",
       "100004                                NaN                            NaN   \n",
       "100005                                NaN                            NaN   \n",
       "\n",
       "            REFUSED_RATE_DOWN_PAYMENT_MEAN  REFUSED_DAYS_DECISION_MIN  \\\n",
       "SK_ID_CURR                                                              \n",
       "100001                                 NaN                        NaN   \n",
       "100002                                 NaN                        NaN   \n",
       "100003                                 NaN                        NaN   \n",
       "100004                                 NaN                        NaN   \n",
       "100005                                 NaN                        NaN   \n",
       "\n",
       "            REFUSED_DAYS_DECISION_MAX  REFUSED_DAYS_DECISION_MEAN  \\\n",
       "SK_ID_CURR                                                          \n",
       "100001                            NaN                         NaN   \n",
       "100002                            NaN                         NaN   \n",
       "100003                            NaN                         NaN   \n",
       "100004                            NaN                         NaN   \n",
       "100005                            NaN                         NaN   \n",
       "\n",
       "            REFUSED_CNT_PAYMENT_MEAN  REFUSED_CNT_PAYMENT_SUM  \n",
       "SK_ID_CURR                                                     \n",
       "100001                           NaN                      NaN  \n",
       "100002                           NaN                      NaN  \n",
       "100003                           NaN                      NaN  \n",
       "100004                           NaN                      NaN  \n",
       "100005                           NaN                      NaN  \n",
       "\n",
       "[5 rows x 249 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_uuid": "73feea739de45e7bc5b88e8de6599c7c63078604"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(338857, 249)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "_uuid": "2c46c180bdefd9d9b78f9350969ce4d5f9ad7b61"
   },
   "outputs": [],
   "source": [
    "pos = pd.read_csv('POS_CASH_balance.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "_uuid": "4c6bf2ed961498e402f81a1753f9617940b189fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10001358, 8)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "_uuid": "15b0c5d4424da318c3559349dcf38a9b82484aa9"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_PREV</th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>CNT_INSTALMENT</th>\n",
       "      <th>CNT_INSTALMENT_FUTURE</th>\n",
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       "      <td>48.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2341044</td>\n",
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       "      <td>-35</td>\n",
       "      <td>36.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_PREV  SK_ID_CURR  MONTHS_BALANCE  CNT_INSTALMENT  \\\n",
       "0     1803195      182943             -31            48.0   \n",
       "1     1715348      367990             -33            36.0   \n",
       "2     1784872      397406             -32            12.0   \n",
       "3     1903291      269225             -35            48.0   \n",
       "4     2341044      334279             -35            36.0   \n",
       "\n",
       "   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  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "_uuid": "a49b3ad8de9a699b5cd12e621268251af9d2bf65"
   },
   "outputs": [],
   "source": [
    "pos, cat_cols = one_hot_encoder(pos, nan_as_category= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "_uuid": "a4ff013dcb354049f182454348b77649d1c82cf5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10001358, 17)"
      ]
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     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pos.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "_uuid": "89c5b49c3b1e6900cb68311948cee38e0c0a8838"
   },
   "outputs": [
    {
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      "text/plain": [
       "   SK_ID_PREV  SK_ID_CURR  MONTHS_BALANCE  CNT_INSTALMENT  \\\n",
       "0     1803195      182943             -31            48.0   \n",
       "1     1715348      367990             -33            36.0   \n",
       "2     1784872      397406             -32            12.0   \n",
       "3     1903291      269225             -35            48.0   \n",
       "4     2341044      334279             -35            36.0   \n",
       "\n",
       "   CNT_INSTALMENT_FUTURE  SK_DPD  SK_DPD_DEF  NAME_CONTRACT_STATUS_Active  \\\n",
       "0                   45.0       0           0                            1   \n",
       "1                   35.0       0           0                            1   \n",
       "2                    9.0       0           0                            1   \n",
       "3                   42.0       0           0                            1   \n",
       "4                   35.0       0           0                            1   \n",
       "\n",
       "   NAME_CONTRACT_STATUS_Amortized debt  NAME_CONTRACT_STATUS_Approved  \\\n",
       "0                                    0                              0   \n",
       "1                                    0                              0   \n",
       "2                                    0                              0   \n",
       "3                                    0                              0   \n",
       "4                                    0                              0   \n",
       "\n",
       "   NAME_CONTRACT_STATUS_Canceled  NAME_CONTRACT_STATUS_Completed  \\\n",
       "0                              0                               0   \n",
       "1                              0                               0   \n",
       "2                              0                               0   \n",
       "3                              0                               0   \n",
       "4                              0                               0   \n",
       "\n",
       "   NAME_CONTRACT_STATUS_Demand  NAME_CONTRACT_STATUS_Returned to the store  \\\n",
       "0                            0                                           0   \n",
       "1                            0                                           0   \n",
       "2                            0                                           0   \n",
       "3                            0                                           0   \n",
       "4                            0                                           0   \n",
       "\n",
       "   NAME_CONTRACT_STATUS_Signed  NAME_CONTRACT_STATUS_XNA  \\\n",
       "0                            0                         0   \n",
       "1                            0                         0   \n",
       "2                            0                         0   \n",
       "3                            0                         0   \n",
       "4                            0                         0   \n",
       "\n",
       "   NAME_CONTRACT_STATUS_nan  \n",
       "0                         0  \n",
       "1                         0  \n",
       "2                         0  \n",
       "3                         0  \n",
       "4                         0  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "_uuid": "f18c631aa253415079e1b8ff13fd77ddaddee8d0"
   },
   "outputs": [],
   "source": [
    "# Features\n",
    "aggregations = {\n",
    "    'MONTHS_BALANCE': ['max', 'mean', 'size'],\n",
    "    'SK_DPD': ['max', 'mean'],\n",
    "    'SK_DPD_DEF': ['max', 'mean']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "_uuid": "a2d5f42b1903db0a7b7fe37a924c99cf1a6617d9"
   },
   "outputs": [],
   "source": [
    "for cat in cat_cols:\n",
    "    aggregations[cat] = ['mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "_uuid": "3c5510f9db5ba18c8e29b6aa07c92785d70cad59"
   },
   "outputs": [],
   "source": [
    "pos_agg = pos.groupby('SK_ID_CURR').agg(aggregations)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "_uuid": "7eb60a6b14afbb0030c15dbb103aefc3a9195627"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">MONTHS_BALANCE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SK_DPD</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SK_DPD_DEF</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Active</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Amortized debt</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Approved</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Canceled</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Completed</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Demand</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Returned to the store</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Signed</th>\n",
       "      <th>NAME_CONTRACT_STATUS_XNA</th>\n",
       "      <th>NAME_CONTRACT_STATUS_nan</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>size</th>\n",
       "      <th>max</th>\n",
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       "      <th>mean</th>\n",
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       "    <tr>\n",
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      ],
      "text/plain": [
       "           MONTHS_BALANCE                 SK_DPD           SK_DPD_DEF  \\\n",
       "                      max       mean size    max      mean        max   \n",
       "SK_ID_CURR                                                              \n",
       "100001                -53 -72.555556    9      7  0.777778          7   \n",
       "100002                 -1 -10.000000   19      0  0.000000          0   \n",
       "100003                -18 -43.785714   28      0  0.000000          0   \n",
       "100004                -24 -25.500000    4      0  0.000000          0   \n",
       "100005                -15 -20.000000   11      0  0.000000          0   \n",
       "\n",
       "                     NAME_CONTRACT_STATUS_Active  \\\n",
       "                mean                        mean   \n",
       "SK_ID_CURR                                         \n",
       "100001      0.777778                    0.777778   \n",
       "100002      0.000000                    1.000000   \n",
       "100003      0.000000                    0.928571   \n",
       "100004      0.000000                    0.750000   \n",
       "100005      0.000000                    0.818182   \n",
       "\n",
       "           NAME_CONTRACT_STATUS_Amortized debt NAME_CONTRACT_STATUS_Approved  \\\n",
       "                                          mean                          mean   \n",
       "SK_ID_CURR                                                                     \n",
       "100001                                     0.0                           0.0   \n",
       "100002                                     0.0                           0.0   \n",
       "100003                                     0.0                           0.0   \n",
       "100004                                     0.0                           0.0   \n",
       "100005                                     0.0                           0.0   \n",
       "\n",
       "           NAME_CONTRACT_STATUS_Canceled NAME_CONTRACT_STATUS_Completed  \\\n",
       "                                    mean                           mean   \n",
       "SK_ID_CURR                                                                \n",
       "100001                               0.0                       0.222222   \n",
       "100002                               0.0                       0.000000   \n",
       "100003                               0.0                       0.071429   \n",
       "100004                               0.0                       0.250000   \n",
       "100005                               0.0                       0.090909   \n",
       "\n",
       "           NAME_CONTRACT_STATUS_Demand  \\\n",
       "                                  mean   \n",
       "SK_ID_CURR                               \n",
       "100001                             0.0   \n",
       "100002                             0.0   \n",
       "100003                             0.0   \n",
       "100004                             0.0   \n",
       "100005                             0.0   \n",
       "\n",
       "           NAME_CONTRACT_STATUS_Returned to the store  \\\n",
       "                                                 mean   \n",
       "SK_ID_CURR                                              \n",
       "100001                                            0.0   \n",
       "100002                                            0.0   \n",
       "100003                                            0.0   \n",
       "100004                                            0.0   \n",
       "100005                                            0.0   \n",
       "\n",
       "           NAME_CONTRACT_STATUS_Signed NAME_CONTRACT_STATUS_XNA  \\\n",
       "                                  mean                     mean   \n",
       "SK_ID_CURR                                                        \n",
       "100001                        0.000000                      0.0   \n",
       "100002                        0.000000                      0.0   \n",
       "100003                        0.000000                      0.0   \n",
       "100004                        0.000000                      0.0   \n",
       "100005                        0.090909                      0.0   \n",
       "\n",
       "           NAME_CONTRACT_STATUS_nan  \n",
       "                               mean  \n",
       "SK_ID_CURR                           \n",
       "100001                            0  \n",
       "100002                            0  \n",
       "100003                            0  \n",
       "100004                            0  \n",
       "100005                            0  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "_uuid": "3c5d48b5c44c3aa6924f09a3aa89d02d1d33b721"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(337252, 17)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "_uuid": "b4d320a8728c120f4519f6ade3e41a539015d02d"
   },
   "outputs": [],
   "source": [
    "pos_agg.columns = pd.Index(['POS_' + e[0] + \"_\" + e[1].upper() for e in pos_agg.columns.tolist()])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "_uuid": "794c3aa7ab916a2041320cb435186fd1b600fce2"
   },
   "outputs": [
    {
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       "      <th>POS_MONTHS_BALANCE_SIZE</th>\n",
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       "      <th>POS_NAME_CONTRACT_STATUS_Approved_MEAN</th>\n",
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       "      <th>POS_NAME_CONTRACT_STATUS_Completed_MEAN</th>\n",
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      ],
      "text/plain": [
       "            POS_MONTHS_BALANCE_MAX  POS_MONTHS_BALANCE_MEAN  \\\n",
       "SK_ID_CURR                                                    \n",
       "100001                         -53               -72.555556   \n",
       "100002                          -1               -10.000000   \n",
       "100003                         -18               -43.785714   \n",
       "100004                         -24               -25.500000   \n",
       "100005                         -15               -20.000000   \n",
       "\n",
       "            POS_MONTHS_BALANCE_SIZE  POS_SK_DPD_MAX  POS_SK_DPD_MEAN  \\\n",
       "SK_ID_CURR                                                             \n",
       "100001                            9               7         0.777778   \n",
       "100002                           19               0         0.000000   \n",
       "100003                           28               0         0.000000   \n",
       "100004                            4               0         0.000000   \n",
       "100005                           11               0         0.000000   \n",
       "\n",
       "            POS_SK_DPD_DEF_MAX  POS_SK_DPD_DEF_MEAN  \\\n",
       "SK_ID_CURR                                            \n",
       "100001                       7             0.777778   \n",
       "100002                       0             0.000000   \n",
       "100003                       0             0.000000   \n",
       "100004                       0             0.000000   \n",
       "100005                       0             0.000000   \n",
       "\n",
       "            POS_NAME_CONTRACT_STATUS_Active_MEAN  \\\n",
       "SK_ID_CURR                                         \n",
       "100001                                  0.777778   \n",
       "100002                                  1.000000   \n",
       "100003                                  0.928571   \n",
       "100004                                  0.750000   \n",
       "100005                                  0.818182   \n",
       "\n",
       "            POS_NAME_CONTRACT_STATUS_Amortized debt_MEAN  \\\n",
       "SK_ID_CURR                                                 \n",
       "100001                                               0.0   \n",
       "100002                                               0.0   \n",
       "100003                                               0.0   \n",
       "100004                                               0.0   \n",
       "100005                                               0.0   \n",
       "\n",
       "            POS_NAME_CONTRACT_STATUS_Approved_MEAN  \\\n",
       "SK_ID_CURR                                           \n",
       "100001                                         0.0   \n",
       "100002                                         0.0   \n",
       "100003                                         0.0   \n",
       "100004                                         0.0   \n",
       "100005                                         0.0   \n",
       "\n",
       "            POS_NAME_CONTRACT_STATUS_Canceled_MEAN  \\\n",
       "SK_ID_CURR                                           \n",
       "100001                                         0.0   \n",
       "100002                                         0.0   \n",
       "100003                                         0.0   \n",
       "100004                                         0.0   \n",
       "100005                                         0.0   \n",
       "\n",
       "            POS_NAME_CONTRACT_STATUS_Completed_MEAN  \\\n",
       "SK_ID_CURR                                            \n",
       "100001                                     0.222222   \n",
       "100002                                     0.000000   \n",
       "100003                                     0.071429   \n",
       "100004                                     0.250000   \n",
       "100005                                     0.090909   \n",
       "\n",
       "            POS_NAME_CONTRACT_STATUS_Demand_MEAN  \\\n",
       "SK_ID_CURR                                         \n",
       "100001                                       0.0   \n",
       "100002                                       0.0   \n",
       "100003                                       0.0   \n",
       "100004                                       0.0   \n",
       "100005                                       0.0   \n",
       "\n",
       "            POS_NAME_CONTRACT_STATUS_Returned to the store_MEAN  \\\n",
       "SK_ID_CURR                                                        \n",
       "100001                                                    0.0     \n",
       "100002                                                    0.0     \n",
       "100003                                                    0.0     \n",
       "100004                                                    0.0     \n",
       "100005                                                    0.0     \n",
       "\n",
       "            POS_NAME_CONTRACT_STATUS_Signed_MEAN  \\\n",
       "SK_ID_CURR                                         \n",
       "100001                                  0.000000   \n",
       "100002                                  0.000000   \n",
       "100003                                  0.000000   \n",
       "100004                                  0.000000   \n",
       "100005                                  0.090909   \n",
       "\n",
       "            POS_NAME_CONTRACT_STATUS_XNA_MEAN  \\\n",
       "SK_ID_CURR                                      \n",
       "100001                                    0.0   \n",
       "100002                                    0.0   \n",
       "100003                                    0.0   \n",
       "100004                                    0.0   \n",
       "100005                                    0.0   \n",
       "\n",
       "            POS_NAME_CONTRACT_STATUS_nan_MEAN  \n",
       "SK_ID_CURR                                     \n",
       "100001                                      0  \n",
       "100002                                      0  \n",
       "100003                                      0  \n",
       "100004                                      0  \n",
       "100005                                      0  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "f0d92fa97586e2bc701a406756b341456af1e409"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "_uuid": "6c28f424bb479c01cfb600ffa43efc251ef41b70"
   },
   "outputs": [],
   "source": [
    "pos_agg['POS_COUNT'] = pos.groupby('SK_ID_CURR').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "_uuid": "b3a3a42a353a13e9e4279b00a8ac510201857826"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_CURR\n",
       "100001     9\n",
       "100002    19\n",
       "100003    28\n",
       "100004     4\n",
       "100005    11\n",
       "Name: POS_COUNT, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_agg['POS_COUNT'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "_uuid": "48291d93ec48e3f5a5d31941a4b1851bc8139c6c"
   },
   "outputs": [],
   "source": [
    "del pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "_uuid": "e4c05565e5b0888b9db9f2af7229c4fe95011836"
   },
   "outputs": [],
   "source": [
    "ins = pd.read_csv('installments_payments.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "_uuid": "7a50e3461da8de450bc1f5557f55b49ae5219e60"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_PREV</th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>NUM_INSTALMENT_VERSION</th>\n",
       "      <th>NUM_INSTALMENT_NUMBER</th>\n",
       "      <th>DAYS_INSTALMENT</th>\n",
       "      <th>DAYS_ENTRY_PAYMENT</th>\n",
       "      <th>AMT_INSTALMENT</th>\n",
       "      <th>AMT_PAYMENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1054186</td>\n",
       "      <td>161674</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>-1180.0</td>\n",
       "      <td>-1187.0</td>\n",
       "      <td>6948.360</td>\n",
       "      <td>6948.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1330831</td>\n",
       "      <td>151639</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-2156.0</td>\n",
       "      <td>-2156.0</td>\n",
       "      <td>1716.525</td>\n",
       "      <td>1716.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2085231</td>\n",
       "      <td>193053</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>-63.0</td>\n",
       "      <td>25425.000</td>\n",
       "      <td>25425.000</td>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>-2426.0</td>\n",
       "      <td>24350.130</td>\n",
       "      <td>24350.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2714724</td>\n",
       "      <td>167756</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1383.0</td>\n",
       "      <td>-1366.0</td>\n",
       "      <td>2165.040</td>\n",
       "      <td>2160.585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_PREV  SK_ID_CURR  NUM_INSTALMENT_VERSION  NUM_INSTALMENT_NUMBER  \\\n",
       "0     1054186      161674                     1.0                      6   \n",
       "1     1330831      151639                     0.0                     34   \n",
       "2     2085231      193053                     2.0                      1   \n",
       "3     2452527      199697                     1.0                      3   \n",
       "4     2714724      167756                     1.0                      2   \n",
       "\n",
       "   DAYS_INSTALMENT  DAYS_ENTRY_PAYMENT  AMT_INSTALMENT  AMT_PAYMENT  \n",
       "0          -1180.0             -1187.0        6948.360     6948.360  \n",
       "1          -2156.0             -2156.0        1716.525     1716.525  \n",
       "2            -63.0               -63.0       25425.000    25425.000  \n",
       "3          -2418.0             -2426.0       24350.130    24350.130  \n",
       "4          -1383.0             -1366.0        2165.040     2160.585  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "_uuid": "432205695423d68a6a4271a22f2c71b7906f05f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13605401, 8)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "_uuid": "9941e9e26fd61ea036ca171d28792af601c94706"
   },
   "outputs": [],
   "source": [
    "ins, cat_cols = one_hot_encoder(ins, nan_as_category= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "_uuid": "33e83bf711c8f0fcbb9ff41736db02ef44878d2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13605401, 8)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "_uuid": "1b5cebf3ce13a36a1b6fa301c36f552bb804b20a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>SK_ID_PREV</th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>NUM_INSTALMENT_VERSION</th>\n",
       "      <th>NUM_INSTALMENT_NUMBER</th>\n",
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       "      <th>DAYS_ENTRY_PAYMENT</th>\n",
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       "      <th>AMT_PAYMENT</th>\n",
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       "      <th>3</th>\n",
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       "      <td>24350.130</td>\n",
       "      <td>24350.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>2165.040</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_PREV  SK_ID_CURR  NUM_INSTALMENT_VERSION  NUM_INSTALMENT_NUMBER  \\\n",
       "0     1054186      161674                     1.0                      6   \n",
       "1     1330831      151639                     0.0                     34   \n",
       "2     2085231      193053                     2.0                      1   \n",
       "3     2452527      199697                     1.0                      3   \n",
       "4     2714724      167756                     1.0                      2   \n",
       "\n",
       "   DAYS_INSTALMENT  DAYS_ENTRY_PAYMENT  AMT_INSTALMENT  AMT_PAYMENT  \n",
       "0          -1180.0             -1187.0        6948.360     6948.360  \n",
       "1          -2156.0             -2156.0        1716.525     1716.525  \n",
       "2            -63.0               -63.0       25425.000    25425.000  \n",
       "3          -2418.0             -2426.0       24350.130    24350.130  \n",
       "4          -1383.0             -1366.0        2165.040     2160.585  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "_uuid": "95cc83adec8489b7e8c6ae401a4bee7bcf07dbb9"
   },
   "outputs": [],
   "source": [
    "# Percentage and difference paid in each installment (amount paid and installment value)\n",
    "ins['PAYMENT_PERC'] = ins['AMT_PAYMENT'] / ins['AMT_INSTALMENT']\n",
    "ins['PAYMENT_DIFF'] = ins['AMT_INSTALMENT'] - ins['AMT_PAYMENT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "_uuid": "2438af2a8f32aba0c9cf4f269895d4df3f940684"
   },
   "outputs": [],
   "source": [
    "# Days past due and days before due (no negative values)\n",
    "ins['DPD'] = ins['DAYS_ENTRY_PAYMENT'] - ins['DAYS_INSTALMENT']\n",
    "ins['DBD'] = ins['DAYS_INSTALMENT'] - ins['DAYS_ENTRY_PAYMENT']\n",
    "ins['DPD'] = ins['DPD'].apply(lambda x: x if x > 0 else 0)\n",
    "ins['DBD'] = ins['DBD'].apply(lambda x: x if x > 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "_uuid": "9ab342262d0ca97301f6e8c5c68b8fdc4ee5cf9e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13605401, 12)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "_uuid": "3642a72c29f1f139c6f93a8afe15a5a0deb46fb6"
   },
   "outputs": [
    {
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       "      <td>0.997942</td>\n",
       "      <td>4.455</td>\n",
       "      <td>17.0</td>\n",
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      "text/plain": [
       "   SK_ID_PREV  SK_ID_CURR  NUM_INSTALMENT_VERSION  NUM_INSTALMENT_NUMBER  \\\n",
       "0     1054186      161674                     1.0                      6   \n",
       "1     1330831      151639                     0.0                     34   \n",
       "2     2085231      193053                     2.0                      1   \n",
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       "4     2714724      167756                     1.0                      2   \n",
       "\n",
       "   DAYS_INSTALMENT  DAYS_ENTRY_PAYMENT  AMT_INSTALMENT  AMT_PAYMENT  \\\n",
       "0          -1180.0             -1187.0        6948.360     6948.360   \n",
       "1          -2156.0             -2156.0        1716.525     1716.525   \n",
       "2            -63.0               -63.0       25425.000    25425.000   \n",
       "3          -2418.0             -2426.0       24350.130    24350.130   \n",
       "4          -1383.0             -1366.0        2165.040     2160.585   \n",
       "\n",
       "   PAYMENT_PERC  PAYMENT_DIFF   DPD  DBD  \n",
       "0      1.000000         0.000   0.0  7.0  \n",
       "1      1.000000         0.000   0.0  0.0  \n",
       "2      1.000000         0.000   0.0  0.0  \n",
       "3      1.000000         0.000   0.0  8.0  \n",
       "4      0.997942         4.455  17.0  0.0  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "_uuid": "bce9411be7d57bd23a3ffb85dc97d8b273f80bf4"
   },
   "outputs": [],
   "source": [
    "aggregations = {\n",
    "        'NUM_INSTALMENT_VERSION': ['nunique'],\n",
    "        'DPD': ['max', 'mean', 'sum'],\n",
    "        'DBD': ['max', 'mean', 'sum'],\n",
    "        'PAYMENT_PERC': ['max', 'mean', 'sum', 'var'],\n",
    "        'PAYMENT_DIFF': ['max', 'mean', 'sum', 'var'],\n",
    "        'AMT_INSTALMENT': ['max', 'mean', 'sum'],\n",
    "        'AMT_PAYMENT': ['min', 'max', 'mean', 'sum'],\n",
    "        'DAYS_ENTRY_PAYMENT': ['max', 'mean', 'sum']\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "_uuid": "75d585c91cf94fc37a55786a9d882fcfc982384c"
   },
   "outputs": [],
   "source": [
    "for cat in cat_cols:\n",
    "    aggregations[cat] = ['mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "_uuid": "28a8e0e03d3dd793785bf249cfc7c72bb3b3f8d4"
   },
   "outputs": [],
   "source": [
    "ins_agg = ins.groupby('SK_ID_CURR').agg(aggregations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "_uuid": "92e753dcd342aa579c8a3d61acf8c1afe02ceb5e"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>NUM_INSTALMENT_VERSION</th>\n",
       "      <th colspan=\"3\" halign=\"left\">DPD</th>\n",
       "      <th colspan=\"3\" halign=\"left\">DBD</th>\n",
       "      <th colspan=\"3\" halign=\"left\">PAYMENT_PERC</th>\n",
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       "      <th colspan=\"3\" halign=\"left\">DAYS_ENTRY_PAYMENT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>nunique</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>max</th>\n",
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       "      <th>sum</th>\n",
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       "      <th>max</th>\n",
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       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>7.0</td>\n",
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       "      <td>17397.900</td>\n",
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       "      <td>41195.925</td>\n",
       "      <td>3951.000</td>\n",
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       "      <td>-1628.0</td>\n",
       "      <td>-2195.000000</td>\n",
       "      <td>-15365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>20.421053</td>\n",
       "      <td>388.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>53093.745</td>\n",
       "      <td>11559.247105</td>\n",
       "      <td>219625.695</td>\n",
       "      <td>9251.775</td>\n",
       "      <td>53093.745</td>\n",
       "      <td>11559.247105</td>\n",
       "      <td>219625.695</td>\n",
       "      <td>-49.0</td>\n",
       "      <td>-315.421053</td>\n",
       "      <td>-5993.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>...</td>\n",
       "      <td>560835.360</td>\n",
       "      <td>64754.586000</td>\n",
       "      <td>1618864.650</td>\n",
       "      <td>6662.970</td>\n",
       "      <td>560835.360</td>\n",
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       "      <td>1618864.650</td>\n",
       "      <td>-544.0</td>\n",
       "      <td>-1385.320000</td>\n",
       "      <td>-34633.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.666667</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10573.965</td>\n",
       "      <td>7096.155000</td>\n",
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       "      <td>-727.0</td>\n",
       "      <td>-761.666667</td>\n",
       "      <td>-2285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>23.666667</td>\n",
       "      <td>213.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>6240.205000</td>\n",
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       "      <td>17656.245</td>\n",
       "      <td>6240.205000</td>\n",
       "      <td>56161.845</td>\n",
       "      <td>-470.0</td>\n",
       "      <td>-609.555556</td>\n",
       "      <td>-5486.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           NUM_INSTALMENT_VERSION   DPD                   DBD             \\\n",
       "                          nunique   max      mean   sum   max       mean   \n",
       "SK_ID_CURR                                                                 \n",
       "100001                          2  11.0  1.571429  11.0  36.0   8.857143   \n",
       "100002                          2   0.0  0.000000   0.0  31.0  20.421053   \n",
       "100003                          2   0.0  0.000000   0.0  14.0   7.160000   \n",
       "100004                          2   0.0  0.000000   0.0  11.0   7.666667   \n",
       "100005                          2   1.0  0.111111   1.0  37.0  23.666667   \n",
       "\n",
       "                  PAYMENT_PERC              ...    AMT_INSTALMENT  \\\n",
       "              sum          max mean   sum   ...               max   \n",
       "SK_ID_CURR                                  ...                     \n",
       "100001       62.0          1.0  1.0   7.0   ...         17397.900   \n",
       "100002      388.0          1.0  1.0  19.0   ...         53093.745   \n",
       "100003      179.0          1.0  1.0  25.0   ...        560835.360   \n",
       "100004       23.0          1.0  1.0   3.0   ...         10573.965   \n",
       "100005      213.0          1.0  1.0   9.0   ...         17656.245   \n",
       "\n",
       "                                      AMT_PAYMENT                            \\\n",
       "                    mean          sum         min         max          mean   \n",
       "SK_ID_CURR                                                                    \n",
       "100001       5885.132143    41195.925    3951.000   17397.900   5885.132143   \n",
       "100002      11559.247105   219625.695    9251.775   53093.745  11559.247105   \n",
       "100003      64754.586000  1618864.650    6662.970  560835.360  64754.586000   \n",
       "100004       7096.155000    21288.465    5357.250   10573.965   7096.155000   \n",
       "100005       6240.205000    56161.845    4813.200   17656.245   6240.205000   \n",
       "\n",
       "                        DAYS_ENTRY_PAYMENT                        \n",
       "                    sum                max         mean      sum  \n",
       "SK_ID_CURR                                                        \n",
       "100001        41195.925            -1628.0 -2195.000000 -15365.0  \n",
       "100002       219625.695              -49.0  -315.421053  -5993.0  \n",
       "100003      1618864.650             -544.0 -1385.320000 -34633.0  \n",
       "100004        21288.465             -727.0  -761.666667  -2285.0  \n",
       "100005        56161.845             -470.0  -609.555556  -5486.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "_uuid": "385eb089c0c2fa77fd71081ac84f9cd3bf48acba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(339587, 25)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "_uuid": "eb0b0d33c852c880d54d4e627dbeb8e54e9599b0"
   },
   "outputs": [],
   "source": [
    "ins_agg.columns = pd.Index(['INSTAL_' + e[0] + \"_\" + e[1].upper() for e in ins_agg.columns.tolist()])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "_uuid": "cfd1e8a55ce7be0c1e18f9c9222dc96b6bd68e5b"
   },
   "outputs": [],
   "source": [
    "ins_agg['INSTAL_COUNT'] = ins.groupby('SK_ID_CURR').size()\n",
    "del ins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "975200b8e2d22d72d22ba46e9455896129a97fd3"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "b8bccce27b9f4fbb32bbd23790cf4b0cd4b50653"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "_uuid": "587f5f1026705dc1783853437b1e9da3dc50d6ba"
   },
   "outputs": [],
   "source": [
    "cc = pd.read_csv('credit_card_balance.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "_uuid": "f08d09d200f91a394a3afe1854ca9c23955e9ca8"
   },
   "outputs": [
    {
     "data": {
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       "      <th>SK_ID_CURR</th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>AMT_BALANCE</th>\n",
       "      <th>AMT_CREDIT_LIMIT_ACTUAL</th>\n",
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       "      <th>AMT_DRAWINGS_POS_CURRENT</th>\n",
       "      <th>AMT_INST_MIN_REGULARITY</th>\n",
       "      <th>...</th>\n",
       "      <th>AMT_RECIVABLE</th>\n",
       "      <th>AMT_TOTAL_RECEIVABLE</th>\n",
       "      <th>CNT_DRAWINGS_ATM_CURRENT</th>\n",
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       "      <th>CNT_DRAWINGS_POS_CURRENT</th>\n",
       "      <th>CNT_INSTALMENT_MATURE_CUM</th>\n",
       "      <th>NAME_CONTRACT_STATUS</th>\n",
       "      <th>SK_DPD</th>\n",
       "      <th>SK_DPD_DEF</th>\n",
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       "      <td>126868</td>\n",
       "      <td>-1</td>\n",
       "      <td>453919.455</td>\n",
       "      <td>450000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11547.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11547.0</td>\n",
       "      <td>22924.890</td>\n",
       "      <td>...</td>\n",
       "      <td>453919.455</td>\n",
       "      <td>453919.455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_PREV  SK_ID_CURR  MONTHS_BALANCE  AMT_BALANCE  \\\n",
       "0     2562384      378907              -6       56.970   \n",
       "1     2582071      363914              -1    63975.555   \n",
       "2     1740877      371185              -7    31815.225   \n",
       "3     1389973      337855              -4   236572.110   \n",
       "4     1891521      126868              -1   453919.455   \n",
       "\n",
       "   AMT_CREDIT_LIMIT_ACTUAL  AMT_DRAWINGS_ATM_CURRENT  AMT_DRAWINGS_CURRENT  \\\n",
       "0                   135000                       0.0                 877.5   \n",
       "1                    45000                    2250.0                2250.0   \n",
       "2                   450000                       0.0                   0.0   \n",
       "3                   225000                    2250.0                2250.0   \n",
       "4                   450000                       0.0               11547.0   \n",
       "\n",
       "   AMT_DRAWINGS_OTHER_CURRENT  AMT_DRAWINGS_POS_CURRENT  \\\n",
       "0                         0.0                     877.5   \n",
       "1                         0.0                       0.0   \n",
       "2                         0.0                       0.0   \n",
       "3                         0.0                       0.0   \n",
       "4                         0.0                   11547.0   \n",
       "\n",
       "   AMT_INST_MIN_REGULARITY     ...      AMT_RECIVABLE  AMT_TOTAL_RECEIVABLE  \\\n",
       "0                 1700.325     ...              0.000                 0.000   \n",
       "1                 2250.000     ...          64875.555             64875.555   \n",
       "2                 2250.000     ...          31460.085             31460.085   \n",
       "3                11795.760     ...         233048.970            233048.970   \n",
       "4                22924.890     ...         453919.455            453919.455   \n",
       "\n",
       "   CNT_DRAWINGS_ATM_CURRENT  CNT_DRAWINGS_CURRENT  CNT_DRAWINGS_OTHER_CURRENT  \\\n",
       "0                       0.0                     1                         0.0   \n",
       "1                       1.0                     1                         0.0   \n",
       "2                       0.0                     0                         0.0   \n",
       "3                       1.0                     1                         0.0   \n",
       "4                       0.0                     1                         0.0   \n",
       "\n",
       "   CNT_DRAWINGS_POS_CURRENT  CNT_INSTALMENT_MATURE_CUM  NAME_CONTRACT_STATUS  \\\n",
       "0                       1.0                       35.0                Active   \n",
       "1                       0.0                       69.0                Active   \n",
       "2                       0.0                       30.0                Active   \n",
       "3                       0.0                       10.0                Active   \n",
       "4                       1.0                      101.0                Active   \n",
       "\n",
       "   SK_DPD  SK_DPD_DEF  \n",
       "0       0           0  \n",
       "1       0           0  \n",
       "2       0           0  \n",
       "3       0           0  \n",
       "4       0           0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "_uuid": "f37aa8262299598d09c7f9c3cf1da9663e4df2bf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3840312, 23)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "_uuid": "0fed2574fde26ed98c281822298bd0304f7ab731"
   },
   "outputs": [],
   "source": [
    "cc, cat_cols = one_hot_encoder(cc, nan_as_category= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "_uuid": "5c1f9e5df68e67addfbb26bdadad228dc8b88377"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>SK_ID_PREV</th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>AMT_BALANCE</th>\n",
       "      <th>AMT_CREDIT_LIMIT_ACTUAL</th>\n",
       "      <th>AMT_DRAWINGS_ATM_CURRENT</th>\n",
       "      <th>AMT_DRAWINGS_CURRENT</th>\n",
       "      <th>AMT_DRAWINGS_OTHER_CURRENT</th>\n",
       "      <th>AMT_DRAWINGS_POS_CURRENT</th>\n",
       "      <th>AMT_INST_MIN_REGULARITY</th>\n",
       "      <th>...</th>\n",
       "      <th>SK_DPD</th>\n",
       "      <th>SK_DPD_DEF</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Active</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Approved</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Completed</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Demand</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Refused</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Sent proposal</th>\n",
       "      <th>NAME_CONTRACT_STATUS_Signed</th>\n",
       "      <th>NAME_CONTRACT_STATUS_nan</th>\n",
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       "      <td>126868</td>\n",
       "      <td>-1</td>\n",
       "      <td>453919.455</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>11547.0</td>\n",
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       "<p>5 rows × 30 columns</p>\n",
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      "text/plain": [
       "   SK_ID_PREV  SK_ID_CURR  MONTHS_BALANCE  AMT_BALANCE  \\\n",
       "0     2562384      378907              -6       56.970   \n",
       "1     2582071      363914              -1    63975.555   \n",
       "2     1740877      371185              -7    31815.225   \n",
       "3     1389973      337855              -4   236572.110   \n",
       "4     1891521      126868              -1   453919.455   \n",
       "\n",
       "   AMT_CREDIT_LIMIT_ACTUAL  AMT_DRAWINGS_ATM_CURRENT  AMT_DRAWINGS_CURRENT  \\\n",
       "0                   135000                       0.0                 877.5   \n",
       "1                    45000                    2250.0                2250.0   \n",
       "2                   450000                       0.0                   0.0   \n",
       "3                   225000                    2250.0                2250.0   \n",
       "4                   450000                       0.0               11547.0   \n",
       "\n",
       "   AMT_DRAWINGS_OTHER_CURRENT  AMT_DRAWINGS_POS_CURRENT  \\\n",
       "0                         0.0                     877.5   \n",
       "1                         0.0                       0.0   \n",
       "2                         0.0                       0.0   \n",
       "3                         0.0                       0.0   \n",
       "4                         0.0                   11547.0   \n",
       "\n",
       "   AMT_INST_MIN_REGULARITY            ...             SK_DPD  SK_DPD_DEF  \\\n",
       "0                 1700.325            ...                  0           0   \n",
       "1                 2250.000            ...                  0           0   \n",
       "2                 2250.000            ...                  0           0   \n",
       "3                11795.760            ...                  0           0   \n",
       "4                22924.890            ...                  0           0   \n",
       "\n",
       "   NAME_CONTRACT_STATUS_Active  NAME_CONTRACT_STATUS_Approved  \\\n",
       "0                            1                              0   \n",
       "1                            1                              0   \n",
       "2                            1                              0   \n",
       "3                            1                              0   \n",
       "4                            1                              0   \n",
       "\n",
       "   NAME_CONTRACT_STATUS_Completed  NAME_CONTRACT_STATUS_Demand  \\\n",
       "0                               0                            0   \n",
       "1                               0                            0   \n",
       "2                               0                            0   \n",
       "3                               0                            0   \n",
       "4                               0                            0   \n",
       "\n",
       "   NAME_CONTRACT_STATUS_Refused  NAME_CONTRACT_STATUS_Sent proposal  \\\n",
       "0                             0                                   0   \n",
       "1                             0                                   0   \n",
       "2                             0                                   0   \n",
       "3                             0                                   0   \n",
       "4                             0                                   0   \n",
       "\n",
       "   NAME_CONTRACT_STATUS_Signed  NAME_CONTRACT_STATUS_nan  \n",
       "0                            0                         0  \n",
       "1                            0                         0  \n",
       "2                            0                         0  \n",
       "3                            0                         0  \n",
       "4                            0                         0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "_uuid": "0c9e2572e923503ae266665bb6e62df1492eaf45"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3840312, 30)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "_uuid": "6692dbb0618abd8524341634444ebea5a93c7f2e"
   },
   "outputs": [],
   "source": [
    "# General aggregations\n",
    "cc.drop(['SK_ID_PREV'], axis= 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "_uuid": "24d2fc9e323499e8e7ddcc11f00487b6b5f020a6"
   },
   "outputs": [],
   "source": [
    "cc_agg = cc.groupby('SK_ID_CURR').agg(['min', 'max', 'mean', 'sum', 'var'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "_uuid": "a362d27abb28b93ac4d47dacef5435ea31fe6cb7"
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100023</th>\n",
       "      <td>-11</td>\n",
       "      <td>-4</td>\n",
       "      <td>-7.5</td>\n",
       "      <td>-60</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           MONTHS_BALANCE                        AMT_BALANCE             \\\n",
       "                      min max  mean   sum    var         min        max   \n",
       "SK_ID_CURR                                                                \n",
       "100006                 -6  -1  -3.5   -21    3.5         0.0       0.00   \n",
       "100011                -75  -2 -38.5 -2849  462.5         0.0  189000.00   \n",
       "100013                -96  -1 -48.5 -4656  776.0         0.0  161420.22   \n",
       "100021                -18  -2 -10.0  -170   25.5         0.0       0.00   \n",
       "100023                -11  -4  -7.5   -60    6.0         0.0       0.00   \n",
       "\n",
       "                                                    ...   \\\n",
       "                    mean          sum           var ...    \n",
       "SK_ID_CURR                                          ...    \n",
       "100006          0.000000        0.000  0.000000e+00 ...    \n",
       "100011      54482.111149  4031676.225  4.641321e+09 ...    \n",
       "100013      18159.919219  1743352.245  1.869473e+09 ...    \n",
       "100021          0.000000        0.000  0.000000e+00 ...    \n",
       "100023          0.000000        0.000  0.000000e+00 ...    \n",
       "\n",
       "           NAME_CONTRACT_STATUS_Signed                    \\\n",
       "                                   min max mean sum  var   \n",
       "SK_ID_CURR                                                 \n",
       "100006                               0   0  0.0   0  0.0   \n",
       "100011                               0   0  0.0   0  0.0   \n",
       "100013                               0   0  0.0   0  0.0   \n",
       "100021                               0   0  0.0   0  0.0   \n",
       "100023                               0   0  0.0   0  0.0   \n",
       "\n",
       "           NAME_CONTRACT_STATUS_nan                    \n",
       "                                min max mean sum  var  \n",
       "SK_ID_CURR                                             \n",
       "100006                            0   0    0   0  0.0  \n",
       "100011                            0   0    0   0  0.0  \n",
       "100013                            0   0    0   0  0.0  \n",
       "100021                            0   0    0   0  0.0  \n",
       "100023                            0   0    0   0  0.0  \n",
       "\n",
       "[5 rows x 140 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "_uuid": "f80ee6be005b9fe5c7a4973799757f35d1705865"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103558, 140)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "_uuid": "b1fa6d58280d713b90723606f20bc5142a503395"
   },
   "outputs": [],
   "source": [
    "cc_agg.columns = pd.Index(['CC_' + e[0] + \"_\" + e[1].upper() for e in cc_agg.columns.tolist()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "_uuid": "3f0089f6049362fc0c392cd2466e44a4be43cc15"
   },
   "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>CC_MONTHS_BALANCE_MIN</th>\n",
       "      <th>CC_MONTHS_BALANCE_MAX</th>\n",
       "      <th>CC_MONTHS_BALANCE_MEAN</th>\n",
       "      <th>CC_MONTHS_BALANCE_SUM</th>\n",
       "      <th>CC_MONTHS_BALANCE_VAR</th>\n",
       "      <th>CC_AMT_BALANCE_MIN</th>\n",
       "      <th>CC_AMT_BALANCE_MAX</th>\n",
       "      <th>CC_AMT_BALANCE_MEAN</th>\n",
       "      <th>CC_AMT_BALANCE_SUM</th>\n",
       "      <th>CC_AMT_BALANCE_VAR</th>\n",
       "      <th>...</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_Signed_MIN</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_Signed_MAX</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_Signed_MEAN</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_Signed_SUM</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_Signed_VAR</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_nan_MIN</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_nan_MAX</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_nan_MEAN</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_nan_SUM</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_nan_VAR</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>100006</th>\n",
       "      <td>-6</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>-21</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100011</th>\n",
       "      <td>-75</td>\n",
       "      <td>-2</td>\n",
       "      <td>-38.5</td>\n",
       "      <td>-2849</td>\n",
       "      <td>462.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>189000.00</td>\n",
       "      <td>54482.111149</td>\n",
       "      <td>4031676.225</td>\n",
       "      <td>4.641321e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100013</th>\n",
       "      <td>-96</td>\n",
       "      <td>-1</td>\n",
       "      <td>-48.5</td>\n",
       "      <td>-4656</td>\n",
       "      <td>776.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161420.22</td>\n",
       "      <td>18159.919219</td>\n",
       "      <td>1743352.245</td>\n",
       "      <td>1.869473e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100021</th>\n",
       "      <td>-18</td>\n",
       "      <td>-2</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-170</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100023</th>\n",
       "      <td>-11</td>\n",
       "      <td>-4</td>\n",
       "      <td>-7.5</td>\n",
       "      <td>-60</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            CC_MONTHS_BALANCE_MIN  CC_MONTHS_BALANCE_MAX  \\\n",
       "SK_ID_CURR                                                 \n",
       "100006                         -6                     -1   \n",
       "100011                        -75                     -2   \n",
       "100013                        -96                     -1   \n",
       "100021                        -18                     -2   \n",
       "100023                        -11                     -4   \n",
       "\n",
       "            CC_MONTHS_BALANCE_MEAN  CC_MONTHS_BALANCE_SUM  \\\n",
       "SK_ID_CURR                                                  \n",
       "100006                        -3.5                    -21   \n",
       "100011                       -38.5                  -2849   \n",
       "100013                       -48.5                  -4656   \n",
       "100021                       -10.0                   -170   \n",
       "100023                        -7.5                    -60   \n",
       "\n",
       "            CC_MONTHS_BALANCE_VAR  CC_AMT_BALANCE_MIN  CC_AMT_BALANCE_MAX  \\\n",
       "SK_ID_CURR                                                                  \n",
       "100006                        3.5                 0.0                0.00   \n",
       "100011                      462.5                 0.0           189000.00   \n",
       "100013                      776.0                 0.0           161420.22   \n",
       "100021                       25.5                 0.0                0.00   \n",
       "100023                        6.0                 0.0                0.00   \n",
       "\n",
       "            CC_AMT_BALANCE_MEAN  CC_AMT_BALANCE_SUM  CC_AMT_BALANCE_VAR  \\\n",
       "SK_ID_CURR                                                                \n",
       "100006                 0.000000               0.000        0.000000e+00   \n",
       "100011             54482.111149         4031676.225        4.641321e+09   \n",
       "100013             18159.919219         1743352.245        1.869473e+09   \n",
       "100021                 0.000000               0.000        0.000000e+00   \n",
       "100023                 0.000000               0.000        0.000000e+00   \n",
       "\n",
       "                         ...                 \\\n",
       "SK_ID_CURR               ...                  \n",
       "100006                   ...                  \n",
       "100011                   ...                  \n",
       "100013                   ...                  \n",
       "100021                   ...                  \n",
       "100023                   ...                  \n",
       "\n",
       "            CC_NAME_CONTRACT_STATUS_Signed_MIN  \\\n",
       "SK_ID_CURR                                       \n",
       "100006                                       0   \n",
       "100011                                       0   \n",
       "100013                                       0   \n",
       "100021                                       0   \n",
       "100023                                       0   \n",
       "\n",
       "            CC_NAME_CONTRACT_STATUS_Signed_MAX  \\\n",
       "SK_ID_CURR                                       \n",
       "100006                                       0   \n",
       "100011                                       0   \n",
       "100013                                       0   \n",
       "100021                                       0   \n",
       "100023                                       0   \n",
       "\n",
       "            CC_NAME_CONTRACT_STATUS_Signed_MEAN  \\\n",
       "SK_ID_CURR                                        \n",
       "100006                                      0.0   \n",
       "100011                                      0.0   \n",
       "100013                                      0.0   \n",
       "100021                                      0.0   \n",
       "100023                                      0.0   \n",
       "\n",
       "            CC_NAME_CONTRACT_STATUS_Signed_SUM  \\\n",
       "SK_ID_CURR                                       \n",
       "100006                                       0   \n",
       "100011                                       0   \n",
       "100013                                       0   \n",
       "100021                                       0   \n",
       "100023                                       0   \n",
       "\n",
       "            CC_NAME_CONTRACT_STATUS_Signed_VAR  \\\n",
       "SK_ID_CURR                                       \n",
       "100006                                     0.0   \n",
       "100011                                     0.0   \n",
       "100013                                     0.0   \n",
       "100021                                     0.0   \n",
       "100023                                     0.0   \n",
       "\n",
       "            CC_NAME_CONTRACT_STATUS_nan_MIN  CC_NAME_CONTRACT_STATUS_nan_MAX  \\\n",
       "SK_ID_CURR                                                                     \n",
       "100006                                    0                                0   \n",
       "100011                                    0                                0   \n",
       "100013                                    0                                0   \n",
       "100021                                    0                                0   \n",
       "100023                                    0                                0   \n",
       "\n",
       "            CC_NAME_CONTRACT_STATUS_nan_MEAN  CC_NAME_CONTRACT_STATUS_nan_SUM  \\\n",
       "SK_ID_CURR                                                                      \n",
       "100006                                     0                                0   \n",
       "100011                                     0                                0   \n",
       "100013                                     0                                0   \n",
       "100021                                     0                                0   \n",
       "100023                                     0                                0   \n",
       "\n",
       "            CC_NAME_CONTRACT_STATUS_nan_VAR  \n",
       "SK_ID_CURR                                   \n",
       "100006                                  0.0  \n",
       "100011                                  0.0  \n",
       "100013                                  0.0  \n",
       "100021                                  0.0  \n",
       "100023                                  0.0  \n",
       "\n",
       "[5 rows x 140 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "_uuid": "cf768b1a4dce15d995f623ea7f933bce047bacd0"
   },
   "outputs": [],
   "source": [
    "cc_agg['CC_COUNT'] = cc.groupby('SK_ID_CURR').size()\n",
    "del cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "0e111d35e0b27b7aae44637af3871392fb5feb47"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "_uuid": "895f5125d0877c4717978beb411db50ad810da85"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507, 260)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "_uuid": "04461900804b0ee6c8bcd8f6c9a320bc8cbd651a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305811, 104)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "_uuid": "47ddb7a27bad352696e330a99b81b159d8f3355f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(338857, 249)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "_uuid": "2d5e40641bd55556523c8e1cea2d962ea5cc1b97"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(337252, 18)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "_uuid": "8ef31112bd21940af3fe4080d1fdf50df92bdc8c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(339587, 26)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins_agg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "_uuid": "a5de9b37008563abbcac9bc99582e9dc6700cba4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103558, 141)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_agg.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "c56a484da4408da569db00c07c919eb0a890f1b4"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "2bae7deb2d56053aa038fa01cb002016d169b4c3"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "_uuid": "421513758627ddad90b59c3e2457b628301bc524"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507, 260)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=app_train\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "_uuid": "b539f8806bdfe4e9751f4973d3ca25646bf47f5c"
   },
   "outputs": [],
   "source": [
    "df = df.join(bureau_agg, how='left', on='SK_ID_CURR')\n",
    "del bureau_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "_uuid": "fdfcc4095edb525f5b3677003b9c73c705ba653f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507, 364)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "_uuid": "4a21800e85d417a8677eda7bbdb72d807d563b1f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507, 613)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.join(prev_agg, how='left', on='SK_ID_CURR')\n",
    "del prev_agg\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "_uuid": "a1e94f0098a21513d0807445fd59b725d2ce975e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507, 631)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.join(pos_agg, how='left', on='SK_ID_CURR')\n",
    "del pos_agg\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "_uuid": "871f4925e18ded297f593e695e49d24837b172d9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507, 657)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.join(ins_agg, how='left', on='SK_ID_CURR')\n",
    "del ins_agg\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "_uuid": "2ebd130b08ca4c2e9bb94ad9c1cd8e6c9bf32e80"
   },
   "outputs": [],
   "source": [
    "df = df.join(cc_agg, how='left', on='SK_ID_CURR')\n",
    "del cc_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "_uuid": "ebde3236095588cdc55f75305893b48fe1f38799"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507, 798)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "_uuid": "ff2dbdea3c7b81c5630865f44e6f81d442e11861"
   },
   "outputs": [],
   "source": [
    "df.to_csv(\"FINALHOMECREDIT.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "_uuid": "f6616d7f67a5fb51539d15ebd256fffa7b2277ce"
   },
   "outputs": [],
   "source": [
    "# import os\n",
    "# print(os.listdir(\"../working\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "_uuid": "add65fadb87317d49a800d8e37faed589e3a1c37"
   },
   "outputs": [],
   "source": [
    "y = df['TARGET']\n",
    "X=df.drop(['TARGET'],axis=1)\n",
    "# X.drop(columns=['Unnamed: 0'],axis=1,inplace=True)\n",
    "# Remove above line of Unnamed: 0 after making changes to application_train_imputed.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# n "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "_uuid": "38df96486fba8348ce883476afddd0772997bec8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507, 797)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "_uuid": "be98a584a14786711485a3c3a821f7ba4d4a9667"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507,)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "_uuid": "f2093091c612ddfdb83dd8e80937297badc3e85b"
   },
   "outputs": [],
   "source": [
    "from lightgbm import LGBMClassifier\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold,train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "02a7adcbaaf321fefa130f1555b57bcd3e7e1ae8"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "_uuid": "67922b60e4864e1c303b7229fd5cd81df8026f36"
   },
   "outputs": [],
   "source": [
    "clf = LGBMClassifier(\n",
    "            nthread=4,\n",
    "            n_estimators=10000,\n",
    "            learning_rate=0.02,\n",
    "            num_leaves=34,\n",
    "            colsample_bytree=0.9497036,\n",
    "            subsample=0.8715623,\n",
    "            max_depth=8,\n",
    "            reg_alpha=0.041545473,\n",
    "            reg_lambda=0.0735294,\n",
    "            min_split_gain=0.0222415,\n",
    "            min_child_weight=39.3259775,\n",
    "            silent=-1,\n",
    "            verbose=-1, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "_uuid": "aaec335c05d6d7e45bf38584d3380feb0c8fb222"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Postive examples in train set: 226145\n",
      "Negative examples in train set: 19860\n",
      "Postive examples in test set: 56537\n",
      "Negative examples in test set: 4965\n"
     ]
    }
   ],
   "source": [
    "X_train_later, X_test, y_train_later, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=1)\n",
    "print(\"Postive examples in train set: {}\".format(np.sum(y_train_later==0)))\n",
    "print(\"Negative examples in train set: {}\".format(np.sum(y_train_later==1)))\n",
    "\n",
    "print(\"Postive examples in test set: {}\".format(np.sum(y_test==0)))\n",
    "print(\"Negative examples in test set: {}\".format(np.sum(y_test==1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "_uuid": "c93aaa5363f7b43b444b9a54a52c8eee6e5607e8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "clf.fit(X_train_later,y_train_later)\n",
    "y_pred=clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "_uuid": "7a29ca258d131a83062e8d198a9e06e0f8842101"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    60817\n",
       "1      685\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf=pd.DataFrame(data=y_pred)\n",
    "newdf.iloc[:,0].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "_uuid": "5e6aceb783d6fb166e95f9dfcdb46919898a4763"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.92      0.99      0.96     56537\n",
      "          1       0.52      0.07      0.13      4965\n",
      "\n",
      "avg / total       0.89      0.92      0.89     61502\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "_uuid": "10f8d5d01c3d031fccb7fd92089064191489ab3b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[56206,   331],\n",
       "       [ 4611,   354]], dtype=int64)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "_uuid": "94ad0bf225e84c5e38dda6fa2cc5817448638ba2"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "top40 = pd.DataFrame({'features': X_train_later.columns, 'importance': clf.feature_importances_}).sort_values('importance', ascending=False).head(40)\n",
    "# sns.barplot(x=top50['importance'], y=top50['features'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "_uuid": "a6acd87376c02544a73e1db5b5fdd7991a2f371a"
   },
   "outputs": [],
   "source": [
    "feat_list=list(top40['features'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EXT_SOURCE_3', 'PAYMENT_RATE', 'EXT_SOURCE_2', 'DAYS_BIRTH', 'EXT_SOURCE_1', 'DAYS_EMPLOYED', 'DAYS_ID_PUBLISH', 'DAYS_EMPLOYED_PERC', 'DAYS_REGISTRATION', 'SK_ID_CURR', 'INSTAL_DBD_MEAN', 'ANNUITY_INCOME_PERC', 'AMT_ANNUITY', 'INSTAL_DAYS_ENTRY_PAYMENT_MAX', 'DAYS_LAST_PHONE_CHANGE', 'ACTIVE_DAYS_CREDIT_UPDATE_MEAN', 'REGION_POPULATION_RELATIVE', 'INCOME_CREDIT_PERC', 'INSTAL_DBD_MAX', 'CLOSED_DAYS_CREDIT_MAX', 'ACTIVE_DAYS_CREDIT_ENDDATE_MIN', 'BURO_DAYS_CREDIT_VAR', 'APPROVED_DAYS_DECISION_MAX', 'INSTAL_AMT_PAYMENT_MIN', 'BURO_DAYS_CREDIT_MAX', 'INSTAL_DBD_SUM', 'CLOSED_DAYS_CREDIT_ENDDATE_MAX', 'INCOME_PER_PERSON', 'CLOSED_DAYS_CREDIT_VAR', 'APPROVED_APP_CREDIT_PERC_VAR', 'ACTIVE_DAYS_CREDIT_MAX', 'PREV_DAYS_DECISION_MAX', 'PREV_APP_CREDIT_PERC_VAR', 'AMT_GOODS_PRICE', 'AMT_CREDIT', 'ACTIVE_DAYS_CREDIT_ENDDATE_MEAN', 'POS_NAME_CONTRACT_STATUS_Active_MEAN', 'CLOSED_AMT_CREDIT_SUM_MEAN', 'BURO_DAYS_CREDIT_ENDDATE_MEAN', 'ACTIVE_DAYS_CREDIT_VAR']\n"
     ]
    }
   ],
   "source": [
    "print(feat_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1719613d550>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.barplot(x=top40['importance'], y=top40['features'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold,train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from imblearn.over_sampling import SMOTE\n",
    "# import fancyimpute as fi\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.metrics import roc_auc_score, roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"FINALHOMECREDIT.csv\")\n",
    "y = df['TARGET']\n",
    "X=df.drop(['TARGET'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307507, 797)\n",
      "(307507,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_later, X_test, y_train_later, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KFold(n_splits=10, random_state=1001, shuffle=True)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folds=KFold(n_splits=10,shuffle=True,random_state=1001)\n",
    "folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create arrays and dataframes to store results\n",
    "oof_preds = np.zeros(X_train_later.shape[0])\n",
    "sub_preds = np.zeros(X_test.shape[0])\n",
    "feature_importance_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(246005,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oof_preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61502,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CODE_GENDER',\n",
       " 'FLAG_OWN_CAR',\n",
       " 'FLAG_OWN_REALTY',\n",
       " 'CNT_CHILDREN',\n",
       " 'AMT_INCOME_TOTAL',\n",
       " 'AMT_CREDIT',\n",
       " 'AMT_ANNUITY',\n",
       " 'AMT_GOODS_PRICE',\n",
       " 'REGION_POPULATION_RELATIVE',\n",
       " 'DAYS_BIRTH',\n",
       " 'DAYS_EMPLOYED',\n",
       " 'DAYS_REGISTRATION',\n",
       " 'DAYS_ID_PUBLISH',\n",
       " 'OWN_CAR_AGE',\n",
       " 'FLAG_MOBIL',\n",
       " 'FLAG_EMP_PHONE',\n",
       " 'FLAG_WORK_PHONE',\n",
       " 'FLAG_CONT_MOBILE',\n",
       " 'FLAG_PHONE',\n",
       " 'FLAG_EMAIL',\n",
       " 'CNT_FAM_MEMBERS',\n",
       " 'REGION_RATING_CLIENT',\n",
       " 'REGION_RATING_CLIENT_W_CITY',\n",
       " 'HOUR_APPR_PROCESS_START',\n",
       " 'REG_REGION_NOT_LIVE_REGION',\n",
       " 'REG_REGION_NOT_WORK_REGION',\n",
       " 'LIVE_REGION_NOT_WORK_REGION',\n",
       " 'REG_CITY_NOT_LIVE_CITY',\n",
       " 'REG_CITY_NOT_WORK_CITY',\n",
       " 'LIVE_CITY_NOT_WORK_CITY',\n",
       " 'EXT_SOURCE_1',\n",
       " 'EXT_SOURCE_2',\n",
       " 'EXT_SOURCE_3',\n",
       " 'APARTMENTS_AVG',\n",
       " 'BASEMENTAREA_AVG',\n",
       " 'YEARS_BEGINEXPLUATATION_AVG',\n",
       " 'YEARS_BUILD_AVG',\n",
       " 'COMMONAREA_AVG',\n",
       " 'ELEVATORS_AVG',\n",
       " 'ENTRANCES_AVG',\n",
       " 'FLOORSMAX_AVG',\n",
       " 'FLOORSMIN_AVG',\n",
       " 'LANDAREA_AVG',\n",
       " 'LIVINGAPARTMENTS_AVG',\n",
       " 'LIVINGAREA_AVG',\n",
       " 'NONLIVINGAPARTMENTS_AVG',\n",
       " 'NONLIVINGAREA_AVG',\n",
       " 'APARTMENTS_MODE',\n",
       " 'BASEMENTAREA_MODE',\n",
       " 'YEARS_BEGINEXPLUATATION_MODE',\n",
       " 'YEARS_BUILD_MODE',\n",
       " 'COMMONAREA_MODE',\n",
       " 'ELEVATORS_MODE',\n",
       " 'ENTRANCES_MODE',\n",
       " 'FLOORSMAX_MODE',\n",
       " 'FLOORSMIN_MODE',\n",
       " 'LANDAREA_MODE',\n",
       " 'LIVINGAPARTMENTS_MODE',\n",
       " 'LIVINGAREA_MODE',\n",
       " 'NONLIVINGAPARTMENTS_MODE',\n",
       " 'NONLIVINGAREA_MODE',\n",
       " 'APARTMENTS_MEDI',\n",
       " 'BASEMENTAREA_MEDI',\n",
       " 'YEARS_BEGINEXPLUATATION_MEDI',\n",
       " 'YEARS_BUILD_MEDI',\n",
       " 'COMMONAREA_MEDI',\n",
       " 'ELEVATORS_MEDI',\n",
       " 'ENTRANCES_MEDI',\n",
       " 'FLOORSMAX_MEDI',\n",
       " 'FLOORSMIN_MEDI',\n",
       " 'LANDAREA_MEDI',\n",
       " 'LIVINGAPARTMENTS_MEDI',\n",
       " 'LIVINGAREA_MEDI',\n",
       " 'NONLIVINGAPARTMENTS_MEDI',\n",
       " 'NONLIVINGAREA_MEDI',\n",
       " 'TOTALAREA_MODE',\n",
       " 'OBS_30_CNT_SOCIAL_CIRCLE',\n",
       " 'DEF_30_CNT_SOCIAL_CIRCLE',\n",
       " 'OBS_60_CNT_SOCIAL_CIRCLE',\n",
       " 'DEF_60_CNT_SOCIAL_CIRCLE',\n",
       " 'DAYS_LAST_PHONE_CHANGE',\n",
       " 'FLAG_DOCUMENT_2',\n",
       " 'FLAG_DOCUMENT_3',\n",
       " 'FLAG_DOCUMENT_4',\n",
       " 'FLAG_DOCUMENT_5',\n",
       " 'FLAG_DOCUMENT_6',\n",
       " 'FLAG_DOCUMENT_7',\n",
       " 'FLAG_DOCUMENT_8',\n",
       " 'FLAG_DOCUMENT_9',\n",
       " 'FLAG_DOCUMENT_10',\n",
       " 'FLAG_DOCUMENT_11',\n",
       " 'FLAG_DOCUMENT_12',\n",
       " 'FLAG_DOCUMENT_13',\n",
       " 'FLAG_DOCUMENT_14',\n",
       " 'FLAG_DOCUMENT_15',\n",
       " 'FLAG_DOCUMENT_16',\n",
       " 'FLAG_DOCUMENT_17',\n",
       " 'FLAG_DOCUMENT_18',\n",
       " 'FLAG_DOCUMENT_19',\n",
       " 'FLAG_DOCUMENT_20',\n",
       " 'FLAG_DOCUMENT_21',\n",
       " 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
       " 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
       " 'AMT_REQ_CREDIT_BUREAU_WEEK',\n",
       " 'AMT_REQ_CREDIT_BUREAU_MON',\n",
       " 'AMT_REQ_CREDIT_BUREAU_QRT',\n",
       " 'AMT_REQ_CREDIT_BUREAU_YEAR',\n",
       " 'DAYS_EMPLOYED_PERC',\n",
       " 'INCOME_CREDIT_PERC',\n",
       " 'INCOME_PER_PERSON',\n",
       " 'ANNUITY_INCOME_PERC',\n",
       " 'PAYMENT_RATE',\n",
       " 'NAME_CONTRACT_TYPE_Cash loans',\n",
       " 'NAME_CONTRACT_TYPE_Revolving loans',\n",
       " 'NAME_CONTRACT_TYPE_nan',\n",
       " 'NAME_TYPE_SUITE_Children',\n",
       " 'NAME_TYPE_SUITE_Family',\n",
       " 'NAME_TYPE_SUITE_Group of people',\n",
       " 'NAME_TYPE_SUITE_Other_A',\n",
       " 'NAME_TYPE_SUITE_Other_B',\n",
       " 'NAME_TYPE_SUITE_Spouse, partner',\n",
       " 'NAME_TYPE_SUITE_Unaccompanied',\n",
       " 'NAME_TYPE_SUITE_nan',\n",
       " 'NAME_INCOME_TYPE_Businessman',\n",
       " 'NAME_INCOME_TYPE_Commercial associate',\n",
       " 'NAME_INCOME_TYPE_Maternity leave',\n",
       " 'NAME_INCOME_TYPE_Pensioner',\n",
       " 'NAME_INCOME_TYPE_State servant',\n",
       " 'NAME_INCOME_TYPE_Student',\n",
       " 'NAME_INCOME_TYPE_Unemployed',\n",
       " 'NAME_INCOME_TYPE_Working',\n",
       " 'NAME_INCOME_TYPE_nan',\n",
       " 'NAME_EDUCATION_TYPE_Academic degree',\n",
       " 'NAME_EDUCATION_TYPE_Higher education',\n",
       " 'NAME_EDUCATION_TYPE_Incomplete higher',\n",
       " 'NAME_EDUCATION_TYPE_Lower secondary',\n",
       " 'NAME_EDUCATION_TYPE_Secondary / secondary special',\n",
       " 'NAME_EDUCATION_TYPE_nan',\n",
       " 'NAME_FAMILY_STATUS_Civil marriage',\n",
       " 'NAME_FAMILY_STATUS_Married',\n",
       " 'NAME_FAMILY_STATUS_Separated',\n",
       " 'NAME_FAMILY_STATUS_Single / not married',\n",
       " 'NAME_FAMILY_STATUS_Unknown',\n",
       " 'NAME_FAMILY_STATUS_Widow',\n",
       " 'NAME_FAMILY_STATUS_nan',\n",
       " 'NAME_HOUSING_TYPE_Co-op apartment',\n",
       " 'NAME_HOUSING_TYPE_House / apartment',\n",
       " 'NAME_HOUSING_TYPE_Municipal apartment',\n",
       " 'NAME_HOUSING_TYPE_Office apartment',\n",
       " 'NAME_HOUSING_TYPE_Rented apartment',\n",
       " 'NAME_HOUSING_TYPE_With parents',\n",
       " 'NAME_HOUSING_TYPE_nan',\n",
       " 'OCCUPATION_TYPE_Accountants',\n",
       " 'OCCUPATION_TYPE_Cleaning staff',\n",
       " 'OCCUPATION_TYPE_Cooking staff',\n",
       " 'OCCUPATION_TYPE_Core staff',\n",
       " 'OCCUPATION_TYPE_Drivers',\n",
       " 'OCCUPATION_TYPE_HR staff',\n",
       " 'OCCUPATION_TYPE_High skill tech staff',\n",
       " 'OCCUPATION_TYPE_IT staff',\n",
       " 'OCCUPATION_TYPE_Laborers',\n",
       " 'OCCUPATION_TYPE_Low-skill Laborers',\n",
       " 'OCCUPATION_TYPE_Managers',\n",
       " 'OCCUPATION_TYPE_Medicine staff',\n",
       " 'OCCUPATION_TYPE_Private service staff',\n",
       " 'OCCUPATION_TYPE_Realty agents',\n",
       " 'OCCUPATION_TYPE_Sales staff',\n",
       " 'OCCUPATION_TYPE_Secretaries',\n",
       " 'OCCUPATION_TYPE_Security staff',\n",
       " 'OCCUPATION_TYPE_Waiters/barmen staff',\n",
       " 'OCCUPATION_TYPE_nan',\n",
       " 'WEEKDAY_APPR_PROCESS_START_FRIDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_MONDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_SATURDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_SUNDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_THURSDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_TUESDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_WEDNESDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_nan',\n",
       " 'ORGANIZATION_TYPE_Advertising',\n",
       " 'ORGANIZATION_TYPE_Agriculture',\n",
       " 'ORGANIZATION_TYPE_Bank',\n",
       " 'ORGANIZATION_TYPE_Business Entity Type 1',\n",
       " 'ORGANIZATION_TYPE_Business Entity Type 2',\n",
       " 'ORGANIZATION_TYPE_Business Entity Type 3',\n",
       " 'ORGANIZATION_TYPE_Cleaning',\n",
       " 'ORGANIZATION_TYPE_Construction',\n",
       " 'ORGANIZATION_TYPE_Culture',\n",
       " 'ORGANIZATION_TYPE_Electricity',\n",
       " 'ORGANIZATION_TYPE_Emergency',\n",
       " 'ORGANIZATION_TYPE_Government',\n",
       " 'ORGANIZATION_TYPE_Hotel',\n",
       " 'ORGANIZATION_TYPE_Housing',\n",
       " 'ORGANIZATION_TYPE_Industry: type 1',\n",
       " 'ORGANIZATION_TYPE_Industry: type 10',\n",
       " 'ORGANIZATION_TYPE_Industry: type 11',\n",
       " 'ORGANIZATION_TYPE_Industry: type 12',\n",
       " 'ORGANIZATION_TYPE_Industry: type 13',\n",
       " 'ORGANIZATION_TYPE_Industry: type 2',\n",
       " 'ORGANIZATION_TYPE_Industry: type 3',\n",
       " 'ORGANIZATION_TYPE_Industry: type 4',\n",
       " 'ORGANIZATION_TYPE_Industry: type 5',\n",
       " 'ORGANIZATION_TYPE_Industry: type 6',\n",
       " 'ORGANIZATION_TYPE_Industry: type 7',\n",
       " 'ORGANIZATION_TYPE_Industry: type 8',\n",
       " 'ORGANIZATION_TYPE_Industry: type 9',\n",
       " 'ORGANIZATION_TYPE_Insurance',\n",
       " 'ORGANIZATION_TYPE_Kindergarten',\n",
       " 'ORGANIZATION_TYPE_Legal Services',\n",
       " 'ORGANIZATION_TYPE_Medicine',\n",
       " 'ORGANIZATION_TYPE_Military',\n",
       " 'ORGANIZATION_TYPE_Mobile',\n",
       " 'ORGANIZATION_TYPE_Other',\n",
       " 'ORGANIZATION_TYPE_Police',\n",
       " 'ORGANIZATION_TYPE_Postal',\n",
       " 'ORGANIZATION_TYPE_Realtor',\n",
       " 'ORGANIZATION_TYPE_Religion',\n",
       " 'ORGANIZATION_TYPE_Restaurant',\n",
       " 'ORGANIZATION_TYPE_School',\n",
       " 'ORGANIZATION_TYPE_Security',\n",
       " 'ORGANIZATION_TYPE_Security Ministries',\n",
       " 'ORGANIZATION_TYPE_Self-employed',\n",
       " 'ORGANIZATION_TYPE_Services',\n",
       " 'ORGANIZATION_TYPE_Telecom',\n",
       " 'ORGANIZATION_TYPE_Trade: type 1',\n",
       " 'ORGANIZATION_TYPE_Trade: type 2',\n",
       " 'ORGANIZATION_TYPE_Trade: type 3',\n",
       " 'ORGANIZATION_TYPE_Trade: type 4',\n",
       " 'ORGANIZATION_TYPE_Trade: type 5',\n",
       " 'ORGANIZATION_TYPE_Trade: type 6',\n",
       " 'ORGANIZATION_TYPE_Trade: type 7',\n",
       " 'ORGANIZATION_TYPE_Transport: type 1',\n",
       " 'ORGANIZATION_TYPE_Transport: type 2',\n",
       " 'ORGANIZATION_TYPE_Transport: type 3',\n",
       " 'ORGANIZATION_TYPE_Transport: type 4',\n",
       " 'ORGANIZATION_TYPE_University',\n",
       " 'ORGANIZATION_TYPE_XNA',\n",
       " 'ORGANIZATION_TYPE_nan',\n",
       " 'FONDKAPREMONT_MODE_not specified',\n",
       " 'FONDKAPREMONT_MODE_org spec account',\n",
       " 'FONDKAPREMONT_MODE_reg oper account',\n",
       " 'FONDKAPREMONT_MODE_reg oper spec account',\n",
       " 'FONDKAPREMONT_MODE_nan',\n",
       " 'HOUSETYPE_MODE_block of flats',\n",
       " 'HOUSETYPE_MODE_specific housing',\n",
       " 'HOUSETYPE_MODE_terraced house',\n",
       " 'HOUSETYPE_MODE_nan',\n",
       " 'WALLSMATERIAL_MODE_Block',\n",
       " 'WALLSMATERIAL_MODE_Mixed',\n",
       " 'WALLSMATERIAL_MODE_Monolithic',\n",
       " 'WALLSMATERIAL_MODE_Others',\n",
       " 'WALLSMATERIAL_MODE_Panel',\n",
       " 'WALLSMATERIAL_MODE_Stone, brick',\n",
       " 'WALLSMATERIAL_MODE_Wooden',\n",
       " 'WALLSMATERIAL_MODE_nan',\n",
       " 'EMERGENCYSTATE_MODE_No',\n",
       " 'EMERGENCYSTATE_MODE_Yes',\n",
       " 'EMERGENCYSTATE_MODE_nan',\n",
       " 'BURO_DAYS_CREDIT_MIN',\n",
       " 'BURO_DAYS_CREDIT_MAX',\n",
       " 'BURO_DAYS_CREDIT_MEAN',\n",
       " 'BURO_DAYS_CREDIT_VAR',\n",
       " 'BURO_DAYS_CREDIT_ENDDATE_MIN',\n",
       " 'BURO_DAYS_CREDIT_ENDDATE_MAX',\n",
       " 'BURO_DAYS_CREDIT_ENDDATE_MEAN',\n",
       " 'BURO_DAYS_CREDIT_UPDATE_MEAN',\n",
       " 'BURO_CREDIT_DAY_OVERDUE_MAX',\n",
       " 'BURO_CREDIT_DAY_OVERDUE_MEAN',\n",
       " 'BURO_AMT_CREDIT_MAX_OVERDUE_MEAN',\n",
       " 'BURO_AMT_CREDIT_SUM_MAX',\n",
       " 'BURO_AMT_CREDIT_SUM_MEAN',\n",
       " 'BURO_AMT_CREDIT_SUM_SUM',\n",
       " 'BURO_AMT_CREDIT_SUM_DEBT_MAX',\n",
       " 'BURO_AMT_CREDIT_SUM_DEBT_MEAN',\n",
       " 'BURO_AMT_CREDIT_SUM_DEBT_SUM',\n",
       " 'BURO_AMT_CREDIT_SUM_OVERDUE_MEAN',\n",
       " 'BURO_AMT_CREDIT_SUM_LIMIT_MEAN',\n",
       " 'BURO_AMT_CREDIT_SUM_LIMIT_SUM',\n",
       " 'BURO_AMT_ANNUITY_MAX',\n",
       " 'BURO_AMT_ANNUITY_MEAN',\n",
       " 'BURO_CNT_CREDIT_PROLONG_SUM',\n",
       " 'BURO_CREDIT_ACTIVE_Active_MEAN',\n",
       " 'BURO_CREDIT_ACTIVE_Bad debt_MEAN',\n",
       " 'BURO_CREDIT_ACTIVE_Closed_MEAN',\n",
       " 'BURO_CREDIT_ACTIVE_Sold_MEAN',\n",
       " 'BURO_CREDIT_ACTIVE_nan_MEAN',\n",
       " 'BURO_CREDIT_CURRENCY_currency 1_MEAN',\n",
       " 'BURO_CREDIT_CURRENCY_currency 2_MEAN',\n",
       " 'BURO_CREDIT_CURRENCY_currency 3_MEAN',\n",
       " 'BURO_CREDIT_CURRENCY_currency 4_MEAN',\n",
       " 'BURO_CREDIT_CURRENCY_nan_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Another type of loan_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Car loan_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Cash loan (non-earmarked)_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Consumer credit_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Credit card_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Interbank credit_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Loan for business development_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Loan for purchase of shares (margin lending)_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Loan for the purchase of equipment_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Loan for working capital replenishment_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Microloan_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Mobile operator loan_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Mortgage_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Real estate loan_MEAN',\n",
       " 'BURO_CREDIT_TYPE_Unknown type of loan_MEAN',\n",
       " 'BURO_CREDIT_TYPE_nan_MEAN',\n",
       " 'BURO_STATUS_0_MEAN_MEAN',\n",
       " 'BURO_STATUS_1_MEAN_MEAN',\n",
       " 'BURO_STATUS_2_MEAN_MEAN',\n",
       " 'BURO_STATUS_3_MEAN_MEAN',\n",
       " 'BURO_STATUS_4_MEAN_MEAN',\n",
       " 'BURO_STATUS_5_MEAN_MEAN',\n",
       " 'BURO_STATUS_C_MEAN_MEAN',\n",
       " 'BURO_STATUS_X_MEAN_MEAN',\n",
       " 'BURO_STATUS_nan_MEAN_MEAN',\n",
       " 'ACTIVE_DAYS_CREDIT_MIN',\n",
       " 'ACTIVE_DAYS_CREDIT_MAX',\n",
       " 'ACTIVE_DAYS_CREDIT_MEAN',\n",
       " 'ACTIVE_DAYS_CREDIT_VAR',\n",
       " 'ACTIVE_DAYS_CREDIT_ENDDATE_MIN',\n",
       " 'ACTIVE_DAYS_CREDIT_ENDDATE_MAX',\n",
       " 'ACTIVE_DAYS_CREDIT_ENDDATE_MEAN',\n",
       " 'ACTIVE_DAYS_CREDIT_UPDATE_MEAN',\n",
       " 'ACTIVE_CREDIT_DAY_OVERDUE_MAX',\n",
       " 'ACTIVE_CREDIT_DAY_OVERDUE_MEAN',\n",
       " 'ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN',\n",
       " 'ACTIVE_AMT_CREDIT_SUM_MAX',\n",
       " 'ACTIVE_AMT_CREDIT_SUM_MEAN',\n",
       " 'ACTIVE_AMT_CREDIT_SUM_SUM',\n",
       " 'ACTIVE_AMT_CREDIT_SUM_DEBT_MAX',\n",
       " 'ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN',\n",
       " 'ACTIVE_AMT_CREDIT_SUM_DEBT_SUM',\n",
       " 'ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN',\n",
       " 'ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN',\n",
       " 'ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM',\n",
       " 'ACTIVE_AMT_ANNUITY_MAX',\n",
       " 'ACTIVE_AMT_ANNUITY_MEAN',\n",
       " 'ACTIVE_CNT_CREDIT_PROLONG_SUM',\n",
       " 'CLOSED_DAYS_CREDIT_MIN',\n",
       " 'CLOSED_DAYS_CREDIT_MAX',\n",
       " 'CLOSED_DAYS_CREDIT_MEAN',\n",
       " 'CLOSED_DAYS_CREDIT_VAR',\n",
       " 'CLOSED_DAYS_CREDIT_ENDDATE_MIN',\n",
       " 'CLOSED_DAYS_CREDIT_ENDDATE_MAX',\n",
       " 'CLOSED_DAYS_CREDIT_ENDDATE_MEAN',\n",
       " 'CLOSED_DAYS_CREDIT_UPDATE_MEAN',\n",
       " 'CLOSED_CREDIT_DAY_OVERDUE_MAX',\n",
       " 'CLOSED_CREDIT_DAY_OVERDUE_MEAN',\n",
       " 'CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN',\n",
       " 'CLOSED_AMT_CREDIT_SUM_MAX',\n",
       " 'CLOSED_AMT_CREDIT_SUM_MEAN',\n",
       " 'CLOSED_AMT_CREDIT_SUM_SUM',\n",
       " 'CLOSED_AMT_CREDIT_SUM_DEBT_MAX',\n",
       " 'CLOSED_AMT_CREDIT_SUM_DEBT_MEAN',\n",
       " 'CLOSED_AMT_CREDIT_SUM_DEBT_SUM',\n",
       " 'CLOSED_AMT_CREDIT_SUM_OVERDUE_MEAN',\n",
       " 'CLOSED_AMT_CREDIT_SUM_LIMIT_MEAN',\n",
       " 'CLOSED_AMT_CREDIT_SUM_LIMIT_SUM',\n",
       " 'CLOSED_AMT_ANNUITY_MAX',\n",
       " 'CLOSED_AMT_ANNUITY_MEAN',\n",
       " 'CLOSED_CNT_CREDIT_PROLONG_SUM',\n",
       " 'PREV_AMT_ANNUITY_MIN',\n",
       " 'PREV_AMT_ANNUITY_MAX',\n",
       " 'PREV_AMT_ANNUITY_MEAN',\n",
       " 'PREV_AMT_APPLICATION_MIN',\n",
       " 'PREV_AMT_APPLICATION_MAX',\n",
       " 'PREV_AMT_APPLICATION_MEAN',\n",
       " 'PREV_AMT_CREDIT_MIN',\n",
       " 'PREV_AMT_CREDIT_MAX',\n",
       " 'PREV_AMT_CREDIT_MEAN',\n",
       " 'PREV_APP_CREDIT_PERC_MIN',\n",
       " 'PREV_APP_CREDIT_PERC_MAX',\n",
       " 'PREV_APP_CREDIT_PERC_MEAN',\n",
       " 'PREV_APP_CREDIT_PERC_VAR',\n",
       " 'PREV_AMT_DOWN_PAYMENT_MIN',\n",
       " 'PREV_AMT_DOWN_PAYMENT_MAX',\n",
       " 'PREV_AMT_DOWN_PAYMENT_MEAN',\n",
       " 'PREV_AMT_GOODS_PRICE_MIN',\n",
       " 'PREV_AMT_GOODS_PRICE_MAX',\n",
       " 'PREV_AMT_GOODS_PRICE_MEAN',\n",
       " 'PREV_HOUR_APPR_PROCESS_START_MIN',\n",
       " 'PREV_HOUR_APPR_PROCESS_START_MAX',\n",
       " 'PREV_HOUR_APPR_PROCESS_START_MEAN',\n",
       " 'PREV_RATE_DOWN_PAYMENT_MIN',\n",
       " 'PREV_RATE_DOWN_PAYMENT_MAX',\n",
       " 'PREV_RATE_DOWN_PAYMENT_MEAN',\n",
       " 'PREV_DAYS_DECISION_MIN',\n",
       " 'PREV_DAYS_DECISION_MAX',\n",
       " 'PREV_DAYS_DECISION_MEAN',\n",
       " 'PREV_CNT_PAYMENT_MEAN',\n",
       " 'PREV_CNT_PAYMENT_SUM',\n",
       " 'PREV_NAME_CONTRACT_TYPE_Cash loans_MEAN',\n",
       " 'PREV_NAME_CONTRACT_TYPE_Consumer loans_MEAN',\n",
       " 'PREV_NAME_CONTRACT_TYPE_Revolving loans_MEAN',\n",
       " 'PREV_NAME_CONTRACT_TYPE_XNA_MEAN',\n",
       " 'PREV_NAME_CONTRACT_TYPE_nan_MEAN',\n",
       " 'PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN',\n",
       " 'PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN',\n",
       " 'PREV_WEEKDAY_APPR_PROCESS_START_SATURDAY_MEAN',\n",
       " 'PREV_WEEKDAY_APPR_PROCESS_START_SUNDAY_MEAN',\n",
       " 'PREV_WEEKDAY_APPR_PROCESS_START_THURSDAY_MEAN',\n",
       " 'PREV_WEEKDAY_APPR_PROCESS_START_TUESDAY_MEAN',\n",
       " 'PREV_WEEKDAY_APPR_PROCESS_START_WEDNESDAY_MEAN',\n",
       " 'PREV_WEEKDAY_APPR_PROCESS_START_nan_MEAN',\n",
       " 'PREV_FLAG_LAST_APPL_PER_CONTRACT_N_MEAN',\n",
       " 'PREV_FLAG_LAST_APPL_PER_CONTRACT_Y_MEAN',\n",
       " 'PREV_FLAG_LAST_APPL_PER_CONTRACT_nan_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Building a house or an annex_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Business development_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Buying a garage_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Buying a home_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Buying a new car_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Buying a used car_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Car repairs_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Education_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Everyday expenses_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Furniture_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Gasification / water supply_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Hobby_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Journey_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Medicine_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Money for a third person_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Other_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Payments on other loans_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Refusal to name the goal_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Repairs_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Urgent needs_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_XAP_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_XNA_MEAN',\n",
       " 'PREV_NAME_CASH_LOAN_PURPOSE_nan_MEAN',\n",
       " 'PREV_NAME_CONTRACT_STATUS_Approved_MEAN',\n",
       " 'PREV_NAME_CONTRACT_STATUS_Canceled_MEAN',\n",
       " 'PREV_NAME_CONTRACT_STATUS_Refused_MEAN',\n",
       " 'PREV_NAME_CONTRACT_STATUS_Unused offer_MEAN',\n",
       " 'PREV_NAME_CONTRACT_STATUS_nan_MEAN',\n",
       " 'PREV_NAME_PAYMENT_TYPE_Cash through the bank_MEAN',\n",
       " 'PREV_NAME_PAYMENT_TYPE_Cashless from the account of the employer_MEAN',\n",
       " 'PREV_NAME_PAYMENT_TYPE_Non-cash from your account_MEAN',\n",
       " 'PREV_NAME_PAYMENT_TYPE_XNA_MEAN',\n",
       " 'PREV_NAME_PAYMENT_TYPE_nan_MEAN',\n",
       " 'PREV_CODE_REJECT_REASON_CLIENT_MEAN',\n",
       " 'PREV_CODE_REJECT_REASON_HC_MEAN',\n",
       " 'PREV_CODE_REJECT_REASON_LIMIT_MEAN',\n",
       " 'PREV_CODE_REJECT_REASON_SCO_MEAN',\n",
       " 'PREV_CODE_REJECT_REASON_SCOFR_MEAN',\n",
       " 'PREV_CODE_REJECT_REASON_SYSTEM_MEAN',\n",
       " 'PREV_CODE_REJECT_REASON_VERIF_MEAN',\n",
       " 'PREV_CODE_REJECT_REASON_XAP_MEAN',\n",
       " 'PREV_CODE_REJECT_REASON_XNA_MEAN',\n",
       " 'PREV_CODE_REJECT_REASON_nan_MEAN',\n",
       " 'PREV_NAME_TYPE_SUITE_Children_MEAN',\n",
       " 'PREV_NAME_TYPE_SUITE_Family_MEAN',\n",
       " 'PREV_NAME_TYPE_SUITE_Group of people_MEAN',\n",
       " 'PREV_NAME_TYPE_SUITE_Other_A_MEAN',\n",
       " 'PREV_NAME_TYPE_SUITE_Other_B_MEAN',\n",
       " 'PREV_NAME_TYPE_SUITE_Spouse, partner_MEAN',\n",
       " 'PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN',\n",
       " 'PREV_NAME_TYPE_SUITE_nan_MEAN',\n",
       " 'PREV_NAME_CLIENT_TYPE_New_MEAN',\n",
       " 'PREV_NAME_CLIENT_TYPE_Refreshed_MEAN',\n",
       " 'PREV_NAME_CLIENT_TYPE_Repeater_MEAN',\n",
       " 'PREV_NAME_CLIENT_TYPE_XNA_MEAN',\n",
       " 'PREV_NAME_CLIENT_TYPE_nan_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Additional Service_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Animals_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Audio/Video_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Auto Accessories_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Clothing and Accessories_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Computers_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Construction Materials_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Consumer Electronics_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Direct Sales_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Education_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Fitness_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Furniture_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Gardening_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Homewares_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_House Construction_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Insurance_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Jewelry_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Medical Supplies_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Medicine_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Mobile_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Office Appliances_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Other_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Photo / Cinema Equipment_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Sport and Leisure_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Tourism_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Vehicles_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_Weapon_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_XNA_MEAN',\n",
       " 'PREV_NAME_GOODS_CATEGORY_nan_MEAN',\n",
       " 'PREV_NAME_PORTFOLIO_Cards_MEAN',\n",
       " 'PREV_NAME_PORTFOLIO_Cars_MEAN',\n",
       " 'PREV_NAME_PORTFOLIO_Cash_MEAN',\n",
       " 'PREV_NAME_PORTFOLIO_POS_MEAN',\n",
       " 'PREV_NAME_PORTFOLIO_XNA_MEAN',\n",
       " 'PREV_NAME_PORTFOLIO_nan_MEAN',\n",
       " 'PREV_NAME_PRODUCT_TYPE_XNA_MEAN',\n",
       " 'PREV_NAME_PRODUCT_TYPE_walk-in_MEAN',\n",
       " 'PREV_NAME_PRODUCT_TYPE_x-sell_MEAN',\n",
       " 'PREV_NAME_PRODUCT_TYPE_nan_MEAN',\n",
       " 'PREV_CHANNEL_TYPE_AP+ (Cash loan)_MEAN',\n",
       " 'PREV_CHANNEL_TYPE_Car dealer_MEAN',\n",
       " 'PREV_CHANNEL_TYPE_Channel of corporate sales_MEAN',\n",
       " 'PREV_CHANNEL_TYPE_Contact center_MEAN',\n",
       " 'PREV_CHANNEL_TYPE_Country-wide_MEAN',\n",
       " 'PREV_CHANNEL_TYPE_Credit and cash offices_MEAN',\n",
       " 'PREV_CHANNEL_TYPE_Regional / Local_MEAN',\n",
       " 'PREV_CHANNEL_TYPE_Stone_MEAN',\n",
       " 'PREV_CHANNEL_TYPE_nan_MEAN',\n",
       " 'PREV_NAME_SELLER_INDUSTRY_Auto technology_MEAN',\n",
       " 'PREV_NAME_SELLER_INDUSTRY_Clothing_MEAN',\n",
       " 'PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN',\n",
       " 'PREV_NAME_SELLER_INDUSTRY_Construction_MEAN',\n",
       " 'PREV_NAME_SELLER_INDUSTRY_Consumer electronics_MEAN',\n",
       " 'PREV_NAME_SELLER_INDUSTRY_Furniture_MEAN',\n",
       " 'PREV_NAME_SELLER_INDUSTRY_Industry_MEAN',\n",
       " 'PREV_NAME_SELLER_INDUSTRY_Jewelry_MEAN',\n",
       " 'PREV_NAME_SELLER_INDUSTRY_MLM partners_MEAN',\n",
       " 'PREV_NAME_SELLER_INDUSTRY_Tourism_MEAN',\n",
       " 'PREV_NAME_SELLER_INDUSTRY_XNA_MEAN',\n",
       " 'PREV_NAME_SELLER_INDUSTRY_nan_MEAN',\n",
       " 'PREV_NAME_YIELD_GROUP_XNA_MEAN',\n",
       " 'PREV_NAME_YIELD_GROUP_high_MEAN',\n",
       " 'PREV_NAME_YIELD_GROUP_low_action_MEAN',\n",
       " 'PREV_NAME_YIELD_GROUP_low_normal_MEAN',\n",
       " 'PREV_NAME_YIELD_GROUP_middle_MEAN',\n",
       " 'PREV_NAME_YIELD_GROUP_nan_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_Card Street_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_Card X-Sell_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_Cash_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_Cash Street: high_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_Cash Street: low_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_Cash Street: middle_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_Cash X-Sell: high_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_Cash X-Sell: low_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_Cash X-Sell: middle_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_POS household with interest_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_POS household without interest_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_POS industry with interest_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_POS industry without interest_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_POS mobile with interest_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_POS mobile without interest_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_POS other with interest_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_POS others without interest_MEAN',\n",
       " 'PREV_PRODUCT_COMBINATION_nan_MEAN',\n",
       " 'APPROVED_AMT_ANNUITY_MIN',\n",
       " 'APPROVED_AMT_ANNUITY_MAX',\n",
       " 'APPROVED_AMT_ANNUITY_MEAN',\n",
       " 'APPROVED_AMT_APPLICATION_MIN',\n",
       " 'APPROVED_AMT_APPLICATION_MAX',\n",
       " 'APPROVED_AMT_APPLICATION_MEAN',\n",
       " 'APPROVED_AMT_CREDIT_MIN',\n",
       " 'APPROVED_AMT_CREDIT_MAX',\n",
       " 'APPROVED_AMT_CREDIT_MEAN',\n",
       " 'APPROVED_APP_CREDIT_PERC_MIN',\n",
       " 'APPROVED_APP_CREDIT_PERC_MAX',\n",
       " 'APPROVED_APP_CREDIT_PERC_MEAN',\n",
       " 'APPROVED_APP_CREDIT_PERC_VAR',\n",
       " 'APPROVED_AMT_DOWN_PAYMENT_MIN',\n",
       " 'APPROVED_AMT_DOWN_PAYMENT_MAX',\n",
       " 'APPROVED_AMT_DOWN_PAYMENT_MEAN',\n",
       " 'APPROVED_AMT_GOODS_PRICE_MIN',\n",
       " 'APPROVED_AMT_GOODS_PRICE_MAX',\n",
       " 'APPROVED_AMT_GOODS_PRICE_MEAN',\n",
       " 'APPROVED_HOUR_APPR_PROCESS_START_MIN',\n",
       " 'APPROVED_HOUR_APPR_PROCESS_START_MAX',\n",
       " 'APPROVED_HOUR_APPR_PROCESS_START_MEAN',\n",
       " 'APPROVED_RATE_DOWN_PAYMENT_MIN',\n",
       " 'APPROVED_RATE_DOWN_PAYMENT_MAX',\n",
       " 'APPROVED_RATE_DOWN_PAYMENT_MEAN',\n",
       " 'APPROVED_DAYS_DECISION_MIN',\n",
       " 'APPROVED_DAYS_DECISION_MAX',\n",
       " 'APPROVED_DAYS_DECISION_MEAN',\n",
       " 'APPROVED_CNT_PAYMENT_MEAN',\n",
       " 'APPROVED_CNT_PAYMENT_SUM',\n",
       " 'REFUSED_AMT_ANNUITY_MIN',\n",
       " 'REFUSED_AMT_ANNUITY_MAX',\n",
       " 'REFUSED_AMT_ANNUITY_MEAN',\n",
       " 'REFUSED_AMT_APPLICATION_MIN',\n",
       " 'REFUSED_AMT_APPLICATION_MAX',\n",
       " 'REFUSED_AMT_APPLICATION_MEAN',\n",
       " 'REFUSED_AMT_CREDIT_MIN',\n",
       " 'REFUSED_AMT_CREDIT_MAX',\n",
       " 'REFUSED_AMT_CREDIT_MEAN',\n",
       " 'REFUSED_APP_CREDIT_PERC_MIN',\n",
       " 'REFUSED_APP_CREDIT_PERC_MAX',\n",
       " 'REFUSED_APP_CREDIT_PERC_MEAN',\n",
       " 'REFUSED_APP_CREDIT_PERC_VAR',\n",
       " 'REFUSED_AMT_DOWN_PAYMENT_MIN',\n",
       " 'REFUSED_AMT_DOWN_PAYMENT_MAX',\n",
       " 'REFUSED_AMT_DOWN_PAYMENT_MEAN',\n",
       " 'REFUSED_AMT_GOODS_PRICE_MIN',\n",
       " 'REFUSED_AMT_GOODS_PRICE_MAX',\n",
       " 'REFUSED_AMT_GOODS_PRICE_MEAN',\n",
       " 'REFUSED_HOUR_APPR_PROCESS_START_MIN',\n",
       " 'REFUSED_HOUR_APPR_PROCESS_START_MAX',\n",
       " 'REFUSED_HOUR_APPR_PROCESS_START_MEAN',\n",
       " 'REFUSED_RATE_DOWN_PAYMENT_MIN',\n",
       " 'REFUSED_RATE_DOWN_PAYMENT_MAX',\n",
       " 'REFUSED_RATE_DOWN_PAYMENT_MEAN',\n",
       " 'REFUSED_DAYS_DECISION_MIN',\n",
       " 'REFUSED_DAYS_DECISION_MAX',\n",
       " 'REFUSED_DAYS_DECISION_MEAN',\n",
       " 'REFUSED_CNT_PAYMENT_MEAN',\n",
       " 'REFUSED_CNT_PAYMENT_SUM',\n",
       " 'POS_MONTHS_BALANCE_MAX',\n",
       " 'POS_MONTHS_BALANCE_MEAN',\n",
       " 'POS_MONTHS_BALANCE_SIZE',\n",
       " 'POS_SK_DPD_MAX',\n",
       " 'POS_SK_DPD_MEAN',\n",
       " 'POS_SK_DPD_DEF_MAX',\n",
       " 'POS_SK_DPD_DEF_MEAN',\n",
       " 'POS_NAME_CONTRACT_STATUS_Active_MEAN',\n",
       " 'POS_NAME_CONTRACT_STATUS_Amortized debt_MEAN',\n",
       " 'POS_NAME_CONTRACT_STATUS_Approved_MEAN',\n",
       " 'POS_NAME_CONTRACT_STATUS_Canceled_MEAN',\n",
       " 'POS_NAME_CONTRACT_STATUS_Completed_MEAN',\n",
       " 'POS_NAME_CONTRACT_STATUS_Demand_MEAN',\n",
       " 'POS_NAME_CONTRACT_STATUS_Returned to the store_MEAN',\n",
       " 'POS_NAME_CONTRACT_STATUS_Signed_MEAN',\n",
       " 'POS_NAME_CONTRACT_STATUS_XNA_MEAN',\n",
       " 'POS_NAME_CONTRACT_STATUS_nan_MEAN',\n",
       " 'POS_COUNT',\n",
       " 'INSTAL_NUM_INSTALMENT_VERSION_NUNIQUE',\n",
       " 'INSTAL_DPD_MAX',\n",
       " 'INSTAL_DPD_MEAN',\n",
       " 'INSTAL_DPD_SUM',\n",
       " 'INSTAL_DBD_MAX',\n",
       " 'INSTAL_DBD_MEAN',\n",
       " 'INSTAL_DBD_SUM',\n",
       " 'INSTAL_PAYMENT_PERC_MAX',\n",
       " 'INSTAL_PAYMENT_PERC_MEAN',\n",
       " 'INSTAL_PAYMENT_PERC_SUM',\n",
       " 'INSTAL_PAYMENT_PERC_VAR',\n",
       " 'INSTAL_PAYMENT_DIFF_MAX',\n",
       " 'INSTAL_PAYMENT_DIFF_MEAN',\n",
       " 'INSTAL_PAYMENT_DIFF_SUM',\n",
       " 'INSTAL_PAYMENT_DIFF_VAR',\n",
       " 'INSTAL_AMT_INSTALMENT_MAX',\n",
       " 'INSTAL_AMT_INSTALMENT_MEAN',\n",
       " 'INSTAL_AMT_INSTALMENT_SUM',\n",
       " 'INSTAL_AMT_PAYMENT_MIN',\n",
       " 'INSTAL_AMT_PAYMENT_MAX',\n",
       " 'INSTAL_AMT_PAYMENT_MEAN',\n",
       " 'INSTAL_AMT_PAYMENT_SUM',\n",
       " 'INSTAL_DAYS_ENTRY_PAYMENT_MAX',\n",
       " 'INSTAL_DAYS_ENTRY_PAYMENT_MEAN',\n",
       " 'INSTAL_DAYS_ENTRY_PAYMENT_SUM',\n",
       " 'INSTAL_COUNT',\n",
       " 'CC_MONTHS_BALANCE_MIN',\n",
       " 'CC_MONTHS_BALANCE_MAX',\n",
       " 'CC_MONTHS_BALANCE_MEAN',\n",
       " 'CC_MONTHS_BALANCE_SUM',\n",
       " 'CC_MONTHS_BALANCE_VAR',\n",
       " 'CC_AMT_BALANCE_MIN',\n",
       " 'CC_AMT_BALANCE_MAX',\n",
       " 'CC_AMT_BALANCE_MEAN',\n",
       " 'CC_AMT_BALANCE_SUM',\n",
       " 'CC_AMT_BALANCE_VAR',\n",
       " 'CC_AMT_CREDIT_LIMIT_ACTUAL_MIN',\n",
       " 'CC_AMT_CREDIT_LIMIT_ACTUAL_MAX',\n",
       " 'CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN',\n",
       " 'CC_AMT_CREDIT_LIMIT_ACTUAL_SUM',\n",
       " 'CC_AMT_CREDIT_LIMIT_ACTUAL_VAR',\n",
       " 'CC_AMT_DRAWINGS_ATM_CURRENT_MIN',\n",
       " 'CC_AMT_DRAWINGS_ATM_CURRENT_MAX',\n",
       " 'CC_AMT_DRAWINGS_ATM_CURRENT_MEAN',\n",
       " 'CC_AMT_DRAWINGS_ATM_CURRENT_SUM',\n",
       " 'CC_AMT_DRAWINGS_ATM_CURRENT_VAR',\n",
       " 'CC_AMT_DRAWINGS_CURRENT_MIN',\n",
       " 'CC_AMT_DRAWINGS_CURRENT_MAX',\n",
       " 'CC_AMT_DRAWINGS_CURRENT_MEAN',\n",
       " 'CC_AMT_DRAWINGS_CURRENT_SUM',\n",
       " 'CC_AMT_DRAWINGS_CURRENT_VAR',\n",
       " 'CC_AMT_DRAWINGS_OTHER_CURRENT_MIN',\n",
       " 'CC_AMT_DRAWINGS_OTHER_CURRENT_MAX',\n",
       " 'CC_AMT_DRAWINGS_OTHER_CURRENT_MEAN',\n",
       " 'CC_AMT_DRAWINGS_OTHER_CURRENT_SUM',\n",
       " 'CC_AMT_DRAWINGS_OTHER_CURRENT_VAR',\n",
       " 'CC_AMT_DRAWINGS_POS_CURRENT_MIN',\n",
       " 'CC_AMT_DRAWINGS_POS_CURRENT_MAX',\n",
       " 'CC_AMT_DRAWINGS_POS_CURRENT_MEAN',\n",
       " 'CC_AMT_DRAWINGS_POS_CURRENT_SUM',\n",
       " 'CC_AMT_DRAWINGS_POS_CURRENT_VAR',\n",
       " 'CC_AMT_INST_MIN_REGULARITY_MIN',\n",
       " 'CC_AMT_INST_MIN_REGULARITY_MAX',\n",
       " 'CC_AMT_INST_MIN_REGULARITY_MEAN',\n",
       " 'CC_AMT_INST_MIN_REGULARITY_SUM',\n",
       " 'CC_AMT_INST_MIN_REGULARITY_VAR',\n",
       " 'CC_AMT_PAYMENT_CURRENT_MIN',\n",
       " 'CC_AMT_PAYMENT_CURRENT_MAX',\n",
       " 'CC_AMT_PAYMENT_CURRENT_MEAN',\n",
       " 'CC_AMT_PAYMENT_CURRENT_SUM',\n",
       " 'CC_AMT_PAYMENT_CURRENT_VAR',\n",
       " 'CC_AMT_PAYMENT_TOTAL_CURRENT_MIN',\n",
       " 'CC_AMT_PAYMENT_TOTAL_CURRENT_MAX',\n",
       " 'CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN',\n",
       " 'CC_AMT_PAYMENT_TOTAL_CURRENT_SUM',\n",
       " 'CC_AMT_PAYMENT_TOTAL_CURRENT_VAR',\n",
       " 'CC_AMT_RECEIVABLE_PRINCIPAL_MIN',\n",
       " 'CC_AMT_RECEIVABLE_PRINCIPAL_MAX',\n",
       " 'CC_AMT_RECEIVABLE_PRINCIPAL_MEAN',\n",
       " 'CC_AMT_RECEIVABLE_PRINCIPAL_SUM',\n",
       " 'CC_AMT_RECEIVABLE_PRINCIPAL_VAR',\n",
       " 'CC_AMT_RECIVABLE_MIN',\n",
       " 'CC_AMT_RECIVABLE_MAX',\n",
       " 'CC_AMT_RECIVABLE_MEAN',\n",
       " 'CC_AMT_RECIVABLE_SUM',\n",
       " 'CC_AMT_RECIVABLE_VAR',\n",
       " 'CC_AMT_TOTAL_RECEIVABLE_MIN',\n",
       " 'CC_AMT_TOTAL_RECEIVABLE_MAX',\n",
       " 'CC_AMT_TOTAL_RECEIVABLE_MEAN',\n",
       " 'CC_AMT_TOTAL_RECEIVABLE_SUM',\n",
       " 'CC_AMT_TOTAL_RECEIVABLE_VAR',\n",
       " 'CC_CNT_DRAWINGS_ATM_CURRENT_MIN',\n",
       " 'CC_CNT_DRAWINGS_ATM_CURRENT_MAX',\n",
       " 'CC_CNT_DRAWINGS_ATM_CURRENT_MEAN',\n",
       " 'CC_CNT_DRAWINGS_ATM_CURRENT_SUM',\n",
       " 'CC_CNT_DRAWINGS_ATM_CURRENT_VAR',\n",
       " 'CC_CNT_DRAWINGS_CURRENT_MIN',\n",
       " 'CC_CNT_DRAWINGS_CURRENT_MAX',\n",
       " 'CC_CNT_DRAWINGS_CURRENT_MEAN',\n",
       " 'CC_CNT_DRAWINGS_CURRENT_SUM',\n",
       " 'CC_CNT_DRAWINGS_CURRENT_VAR',\n",
       " 'CC_CNT_DRAWINGS_OTHER_CURRENT_MIN',\n",
       " 'CC_CNT_DRAWINGS_OTHER_CURRENT_MAX',\n",
       " 'CC_CNT_DRAWINGS_OTHER_CURRENT_MEAN',\n",
       " 'CC_CNT_DRAWINGS_OTHER_CURRENT_SUM',\n",
       " 'CC_CNT_DRAWINGS_OTHER_CURRENT_VAR',\n",
       " 'CC_CNT_DRAWINGS_POS_CURRENT_MIN',\n",
       " 'CC_CNT_DRAWINGS_POS_CURRENT_MAX',\n",
       " 'CC_CNT_DRAWINGS_POS_CURRENT_MEAN',\n",
       " 'CC_CNT_DRAWINGS_POS_CURRENT_SUM',\n",
       " 'CC_CNT_DRAWINGS_POS_CURRENT_VAR',\n",
       " 'CC_CNT_INSTALMENT_MATURE_CUM_MIN',\n",
       " 'CC_CNT_INSTALMENT_MATURE_CUM_MAX',\n",
       " 'CC_CNT_INSTALMENT_MATURE_CUM_MEAN',\n",
       " 'CC_CNT_INSTALMENT_MATURE_CUM_SUM',\n",
       " 'CC_CNT_INSTALMENT_MATURE_CUM_VAR',\n",
       " 'CC_SK_DPD_MIN',\n",
       " 'CC_SK_DPD_MAX',\n",
       " 'CC_SK_DPD_MEAN',\n",
       " 'CC_SK_DPD_SUM',\n",
       " 'CC_SK_DPD_VAR',\n",
       " 'CC_SK_DPD_DEF_MIN',\n",
       " 'CC_SK_DPD_DEF_MAX',\n",
       " 'CC_SK_DPD_DEF_MEAN',\n",
       " 'CC_SK_DPD_DEF_SUM',\n",
       " 'CC_SK_DPD_DEF_VAR',\n",
       " 'CC_NAME_CONTRACT_STATUS_Active_MIN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Active_MAX',\n",
       " 'CC_NAME_CONTRACT_STATUS_Active_MEAN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Active_SUM',\n",
       " 'CC_NAME_CONTRACT_STATUS_Active_VAR',\n",
       " 'CC_NAME_CONTRACT_STATUS_Approved_MIN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Approved_MAX',\n",
       " 'CC_NAME_CONTRACT_STATUS_Approved_MEAN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Approved_SUM',\n",
       " 'CC_NAME_CONTRACT_STATUS_Approved_VAR',\n",
       " 'CC_NAME_CONTRACT_STATUS_Completed_MIN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Completed_MAX',\n",
       " 'CC_NAME_CONTRACT_STATUS_Completed_MEAN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Completed_SUM',\n",
       " 'CC_NAME_CONTRACT_STATUS_Completed_VAR',\n",
       " 'CC_NAME_CONTRACT_STATUS_Demand_MIN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Demand_MAX',\n",
       " 'CC_NAME_CONTRACT_STATUS_Demand_MEAN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Demand_SUM',\n",
       " 'CC_NAME_CONTRACT_STATUS_Demand_VAR',\n",
       " 'CC_NAME_CONTRACT_STATUS_Refused_MIN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Refused_MAX',\n",
       " 'CC_NAME_CONTRACT_STATUS_Refused_MEAN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Refused_SUM',\n",
       " 'CC_NAME_CONTRACT_STATUS_Refused_VAR',\n",
       " 'CC_NAME_CONTRACT_STATUS_Sent proposal_MIN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Sent proposal_MAX',\n",
       " 'CC_NAME_CONTRACT_STATUS_Sent proposal_MEAN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Sent proposal_SUM',\n",
       " 'CC_NAME_CONTRACT_STATUS_Sent proposal_VAR',\n",
       " 'CC_NAME_CONTRACT_STATUS_Signed_MIN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Signed_MAX',\n",
       " 'CC_NAME_CONTRACT_STATUS_Signed_MEAN',\n",
       " 'CC_NAME_CONTRACT_STATUS_Signed_SUM',\n",
       " 'CC_NAME_CONTRACT_STATUS_Signed_VAR',\n",
       " 'CC_NAME_CONTRACT_STATUS_nan_MIN',\n",
       " 'CC_NAME_CONTRACT_STATUS_nan_MAX',\n",
       " 'CC_NAME_CONTRACT_STATUS_nan_MEAN',\n",
       " 'CC_NAME_CONTRACT_STATUS_nan_SUM',\n",
       " 'CC_NAME_CONTRACT_STATUS_nan_VAR',\n",
       " 'CC_COUNT']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feats = [f for f in X_train_later.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]\n",
    "feats    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [     0      1      3 ... 246002 246003 246004] [     2     39     46 ... 245979 245983 245994]\n",
      "24601\n",
      "221404\n",
      "1 [     0      1      2 ... 246002 246003 246004] [     8     10     19 ... 245971 245982 245999]\n",
      "24601\n",
      "221404\n",
      "2 [     0      1      2 ... 246002 246003 246004] [     5     25     36 ... 245988 245991 246000]\n",
      "24601\n",
      "221404\n",
      "3 [     1      2      4 ... 246002 246003 246004] [     0      3      6 ... 245977 245985 245997]\n",
      "24601\n",
      "221404\n",
      "4 [     0      1      2 ... 246001 246003 246004] [    13     14     15 ... 245957 245989 246002]\n",
      "24601\n",
      "221404\n",
      "5 [     0      1      2 ... 246002 246003 246004] [     9     22     43 ... 245992 245995 245996]\n",
      "24600\n",
      "221405\n",
      "6 [     0      1      2 ... 246001 246002 246004] [     4     20     33 ... 245987 245993 246003]\n",
      "24600\n",
      "221405\n",
      "7 [     0      1      2 ... 246000 246002 246003] [    11     18     31 ... 245998 246001 246004]\n",
      "24600\n",
      "221405\n",
      "8 [     0      2      3 ... 246002 246003 246004] [     1     21     24 ... 245970 245978 245980]\n",
      "24600\n",
      "221405\n",
      "9 [     0      1      2 ... 246002 246003 246004] [     7     12     27 ... 245975 245984 245986]\n",
      "24600\n",
      "221405\n"
     ]
    }
   ],
   "source": [
    "for n_fold, (train_idx, valid_idx) in enumerate(folds.split(X_train_later[feats], y_train_later)):\n",
    "    print(n_fold,train_idx,valid_idx)\n",
    "    print(len(valid_idx))\n",
    "    print(len(train_idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds.\n",
      "[200]\ttraining's binary_logloss: 0.234088\ttraining's auc: 0.800519\tvalid_1's binary_logloss: 0.244138\tvalid_1's auc: 0.770119\n",
      "[400]\ttraining's binary_logloss: 0.223443\ttraining's auc: 0.825688\tvalid_1's binary_logloss: 0.240341\tvalid_1's auc: 0.779847\n",
      "[600]\ttraining's binary_logloss: 0.216754\ttraining's auc: 0.841532\tvalid_1's binary_logloss: 0.239289\tvalid_1's auc: 0.782843\n",
      "[800]\ttraining's binary_logloss: 0.2111\ttraining's auc: 0.854851\tvalid_1's binary_logloss: 0.238681\tvalid_1's auc: 0.784653\n",
      "[1000]\ttraining's binary_logloss: 0.205943\ttraining's auc: 0.866688\tvalid_1's binary_logloss: 0.238397\tvalid_1's auc: 0.785546\n",
      "[1200]\ttraining's binary_logloss: 0.201206\ttraining's auc: 0.877093\tvalid_1's binary_logloss: 0.238244\tvalid_1's auc: 0.786072\n",
      "[1400]\ttraining's binary_logloss: 0.196745\ttraining's auc: 0.886689\tvalid_1's binary_logloss: 0.238255\tvalid_1's auc: 0.786204\n",
      "Early stopping, best iteration is:\n",
      "[1208]\ttraining's binary_logloss: 0.201011\ttraining's auc: 0.877497\tvalid_1's binary_logloss: 0.238229\tvalid_1's auc: 0.78613\n",
      "best iteration of clf is :  1208\n",
      "Fold  1 AUC : 0.786130\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[200]\ttraining's binary_logloss: 0.233968\ttraining's auc: 0.80114\tvalid_1's binary_logloss: 0.244836\tvalid_1's auc: 0.766453\n",
      "[400]\ttraining's binary_logloss: 0.223438\ttraining's auc: 0.825674\tvalid_1's binary_logloss: 0.241167\tvalid_1's auc: 0.776236\n",
      "[600]\ttraining's binary_logloss: 0.216548\ttraining's auc: 0.842221\tvalid_1's binary_logloss: 0.240223\tvalid_1's auc: 0.779073\n",
      "[800]\ttraining's binary_logloss: 0.211074\ttraining's auc: 0.855263\tvalid_1's binary_logloss: 0.239906\tvalid_1's auc: 0.780149\n",
      "[1000]\ttraining's binary_logloss: 0.205916\ttraining's auc: 0.867116\tvalid_1's binary_logloss: 0.239963\tvalid_1's auc: 0.780213\n",
      "Early stopping, best iteration is:\n",
      "[819]\ttraining's binary_logloss: 0.210558\ttraining's auc: 0.856451\tvalid_1's binary_logloss: 0.239888\tvalid_1's auc: 0.780281\n",
      "best iteration of clf is :  819\n",
      "Fold  2 AUC : 0.780281\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[200]\ttraining's binary_logloss: 0.234818\ttraining's auc: 0.800115\tvalid_1's binary_logloss: 0.237998\tvalid_1's auc: 0.776426\n",
      "[400]\ttraining's binary_logloss: 0.224192\ttraining's auc: 0.824978\tvalid_1's binary_logloss: 0.234433\tvalid_1's auc: 0.785058\n",
      "[600]\ttraining's binary_logloss: 0.217274\ttraining's auc: 0.841671\tvalid_1's binary_logloss: 0.233376\tvalid_1's auc: 0.78799\n",
      "[800]\ttraining's binary_logloss: 0.211497\ttraining's auc: 0.855451\tvalid_1's binary_logloss: 0.23291\tvalid_1's auc: 0.789175\n",
      "[1000]\ttraining's binary_logloss: 0.206612\ttraining's auc: 0.866769\tvalid_1's binary_logloss: 0.232759\tvalid_1's auc: 0.789668\n",
      "[1200]\ttraining's binary_logloss: 0.201741\ttraining's auc: 0.877537\tvalid_1's binary_logloss: 0.232654\tvalid_1's auc: 0.789831\n",
      "[1400]\ttraining's binary_logloss: 0.197005\ttraining's auc: 0.88751\tvalid_1's binary_logloss: 0.232621\tvalid_1's auc: 0.790103\n",
      "Early stopping, best iteration is:\n",
      "[1268]\ttraining's binary_logloss: 0.200116\ttraining's auc: 0.880975\tvalid_1's binary_logloss: 0.232581\tvalid_1's auc: 0.790097\n",
      "best iteration of clf is :  1268\n",
      "Fold  3 AUC : 0.790097\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[200]\ttraining's binary_logloss: 0.233847\ttraining's auc: 0.801335\tvalid_1's binary_logloss: 0.244853\tvalid_1's auc: 0.768967\n",
      "[400]\ttraining's binary_logloss: 0.223425\ttraining's auc: 0.825541\tvalid_1's binary_logloss: 0.24142\tvalid_1's auc: 0.777969\n",
      "[600]\ttraining's binary_logloss: 0.216489\ttraining's auc: 0.842221\tvalid_1's binary_logloss: 0.240404\tvalid_1's auc: 0.780766\n",
      "[800]\ttraining's binary_logloss: 0.211\ttraining's auc: 0.855181\tvalid_1's binary_logloss: 0.23992\tvalid_1's auc: 0.782399\n",
      "[1000]\ttraining's binary_logloss: 0.206024\ttraining's auc: 0.866571\tvalid_1's binary_logloss: 0.239821\tvalid_1's auc: 0.782606\n",
      "[1200]\ttraining's binary_logloss: 0.201301\ttraining's auc: 0.877125\tvalid_1's binary_logloss: 0.239785\tvalid_1's auc: 0.782939\n",
      "[1400]\ttraining's binary_logloss: 0.196776\ttraining's auc: 0.886687\tvalid_1's binary_logloss: 0.239684\tvalid_1's auc: 0.783177\n",
      "[1600]\ttraining's binary_logloss: 0.192382\ttraining's auc: 0.895777\tvalid_1's binary_logloss: 0.239674\tvalid_1's auc: 0.783149\n",
      "Early stopping, best iteration is:\n",
      "[1466]\ttraining's binary_logloss: 0.195283\ttraining's auc: 0.889817\tvalid_1's binary_logloss: 0.239625\tvalid_1's auc: 0.783339\n",
      "best iteration of clf is :  1466\n",
      "Fold  4 AUC : 0.783339\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[200]\ttraining's binary_logloss: 0.234252\ttraining's auc: 0.800504\tvalid_1's binary_logloss: 0.242945\tvalid_1's auc: 0.76876\n",
      "[400]\ttraining's binary_logloss: 0.223855\ttraining's auc: 0.824923\tvalid_1's binary_logloss: 0.239225\tvalid_1's auc: 0.779168\n",
      "[600]\ttraining's binary_logloss: 0.216999\ttraining's auc: 0.841609\tvalid_1's binary_logloss: 0.238041\tvalid_1's auc: 0.782879\n",
      "[800]\ttraining's binary_logloss: 0.211329\ttraining's auc: 0.855247\tvalid_1's binary_logloss: 0.237411\tvalid_1's auc: 0.784996\n",
      "[1000]\ttraining's binary_logloss: 0.206106\ttraining's auc: 0.867229\tvalid_1's binary_logloss: 0.237143\tvalid_1's auc: 0.785736\n",
      "[1200]\ttraining's binary_logloss: 0.201598\ttraining's auc: 0.877327\tvalid_1's binary_logloss: 0.236947\tvalid_1's auc: 0.786254\n",
      "[1400]\ttraining's binary_logloss: 0.197235\ttraining's auc: 0.886637\tvalid_1's binary_logloss: 0.236855\tvalid_1's auc: 0.786361\n",
      "[1600]\ttraining's binary_logloss: 0.193084\ttraining's auc: 0.895161\tvalid_1's binary_logloss: 0.236824\tvalid_1's auc: 0.786453\n",
      "[1800]\ttraining's binary_logloss: 0.189055\ttraining's auc: 0.90306\tvalid_1's binary_logloss: 0.236842\tvalid_1's auc: 0.786389\n",
      "Early stopping, best iteration is:\n",
      "[1631]\ttraining's binary_logloss: 0.192502\ttraining's auc: 0.896338\tvalid_1's binary_logloss: 0.236806\tvalid_1's auc: 0.786487\n",
      "best iteration of clf is :  1631\n",
      "Fold  5 AUC : 0.786487\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[200]\ttraining's binary_logloss: 0.234492\ttraining's auc: 0.800071\tvalid_1's binary_logloss: 0.241288\tvalid_1's auc: 0.771029\n",
      "[400]\ttraining's binary_logloss: 0.224113\ttraining's auc: 0.824774\tvalid_1's binary_logloss: 0.237341\tvalid_1's auc: 0.780698\n",
      "[600]\ttraining's binary_logloss: 0.217045\ttraining's auc: 0.84188\tvalid_1's binary_logloss: 0.236043\tvalid_1's auc: 0.78415\n",
      "[800]\ttraining's binary_logloss: 0.211341\ttraining's auc: 0.855558\tvalid_1's binary_logloss: 0.235647\tvalid_1's auc: 0.784957\n",
      "[1000]\ttraining's binary_logloss: 0.206294\ttraining's auc: 0.867071\tvalid_1's binary_logloss: 0.235426\tvalid_1's auc: 0.785538\n",
      "[1200]\ttraining's binary_logloss: 0.201532\ttraining's auc: 0.877615\tvalid_1's binary_logloss: 0.235277\tvalid_1's auc: 0.785969\n",
      "[1400]\ttraining's binary_logloss: 0.196972\ttraining's auc: 0.887278\tvalid_1's binary_logloss: 0.235335\tvalid_1's auc: 0.785883\n",
      "Early stopping, best iteration is:\n",
      "[1269]\ttraining's binary_logloss: 0.199852\ttraining's auc: 0.88124\tvalid_1's binary_logloss: 0.235257\tvalid_1's auc: 0.786129\n",
      "best iteration of clf is :  1269\n",
      "Fold  6 AUC : 0.786129\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[200]\ttraining's binary_logloss: 0.234625\ttraining's auc: 0.799369\tvalid_1's binary_logloss: 0.240089\tvalid_1's auc: 0.783299\n",
      "[400]\ttraining's binary_logloss: 0.224218\ttraining's auc: 0.82404\tvalid_1's binary_logloss: 0.235582\tvalid_1's auc: 0.793302\n",
      "[600]\ttraining's binary_logloss: 0.217497\ttraining's auc: 0.840695\tvalid_1's binary_logloss: 0.234336\tvalid_1's auc: 0.796169\n",
      "[800]\ttraining's binary_logloss: 0.211878\ttraining's auc: 0.85425\tvalid_1's binary_logloss: 0.233657\tvalid_1's auc: 0.797867\n",
      "[1000]\ttraining's binary_logloss: 0.20683\ttraining's auc: 0.866076\tvalid_1's binary_logloss: 0.23326\tvalid_1's auc: 0.798844\n",
      "[1200]\ttraining's binary_logloss: 0.202239\ttraining's auc: 0.87629\tvalid_1's binary_logloss: 0.233\tvalid_1's auc: 0.799258\n",
      "[1400]\ttraining's binary_logloss: 0.197802\ttraining's auc: 0.885876\tvalid_1's binary_logloss: 0.232899\tvalid_1's auc: 0.799504\n",
      "[1600]\ttraining's binary_logloss: 0.193493\ttraining's auc: 0.894781\tvalid_1's binary_logloss: 0.232832\tvalid_1's auc: 0.799609\n",
      "Early stopping, best iteration is:\n",
      "[1503]\ttraining's binary_logloss: 0.19545\ttraining's auc: 0.890861\tvalid_1's binary_logloss: 0.232829\tvalid_1's auc: 0.799691\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best iteration of clf is :  1503\n",
      "Fold  7 AUC : 0.799691\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[200]\ttraining's binary_logloss: 0.234292\ttraining's auc: 0.800693\tvalid_1's binary_logloss: 0.240939\tvalid_1's auc: 0.774636\n",
      "[400]\ttraining's binary_logloss: 0.223862\ttraining's auc: 0.825242\tvalid_1's binary_logloss: 0.237622\tvalid_1's auc: 0.782686\n",
      "[600]\ttraining's binary_logloss: 0.217086\ttraining's auc: 0.841611\tvalid_1's binary_logloss: 0.236595\tvalid_1's auc: 0.785242\n",
      "[800]\ttraining's binary_logloss: 0.211383\ttraining's auc: 0.855059\tvalid_1's binary_logloss: 0.236152\tvalid_1's auc: 0.7863\n",
      "[1000]\ttraining's binary_logloss: 0.206345\ttraining's auc: 0.866533\tvalid_1's binary_logloss: 0.235912\tvalid_1's auc: 0.786952\n",
      "[1200]\ttraining's binary_logloss: 0.201485\ttraining's auc: 0.877475\tvalid_1's binary_logloss: 0.235862\tvalid_1's auc: 0.786954\n",
      "Early stopping, best iteration is:\n",
      "[1136]\ttraining's binary_logloss: 0.203029\ttraining's auc: 0.87405\tvalid_1's binary_logloss: 0.235792\tvalid_1's auc: 0.787137\n",
      "best iteration of clf is :  1136\n",
      "Fold  8 AUC : 0.787137\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[200]\ttraining's binary_logloss: 0.234328\ttraining's auc: 0.799365\tvalid_1's binary_logloss: 0.241774\tvalid_1's auc: 0.787052\n",
      "[400]\ttraining's binary_logloss: 0.223908\ttraining's auc: 0.824051\tvalid_1's binary_logloss: 0.237596\tvalid_1's auc: 0.795878\n",
      "[600]\ttraining's binary_logloss: 0.217117\ttraining's auc: 0.840598\tvalid_1's binary_logloss: 0.236004\tvalid_1's auc: 0.799944\n",
      "[800]\ttraining's binary_logloss: 0.211493\ttraining's auc: 0.85432\tvalid_1's binary_logloss: 0.235302\tvalid_1's auc: 0.801974\n",
      "[1000]\ttraining's binary_logloss: 0.206603\ttraining's auc: 0.865753\tvalid_1's binary_logloss: 0.234857\tvalid_1's auc: 0.802957\n",
      "[1200]\ttraining's binary_logloss: 0.202053\ttraining's auc: 0.875955\tvalid_1's binary_logloss: 0.234579\tvalid_1's auc: 0.803714\n",
      "[1400]\ttraining's binary_logloss: 0.197693\ttraining's auc: 0.885375\tvalid_1's binary_logloss: 0.234504\tvalid_1's auc: 0.803873\n",
      "[1600]\ttraining's binary_logloss: 0.193458\ttraining's auc: 0.894039\tvalid_1's binary_logloss: 0.234447\tvalid_1's auc: 0.80389\n",
      "[1800]\ttraining's binary_logloss: 0.189362\ttraining's auc: 0.902108\tvalid_1's binary_logloss: 0.234306\tvalid_1's auc: 0.804184\n",
      "Early stopping, best iteration is:\n",
      "[1783]\ttraining's binary_logloss: 0.189721\ttraining's auc: 0.90144\tvalid_1's binary_logloss: 0.234301\tvalid_1's auc: 0.804255\n",
      "best iteration of clf is :  1783\n",
      "Fold  9 AUC : 0.804255\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[200]\ttraining's binary_logloss: 0.233728\ttraining's auc: 0.800682\tvalid_1's binary_logloss: 0.247729\tvalid_1's auc: 0.766183\n",
      "[400]\ttraining's binary_logloss: 0.223304\ttraining's auc: 0.825267\tvalid_1's binary_logloss: 0.243232\tvalid_1's auc: 0.777924\n",
      "[600]\ttraining's binary_logloss: 0.216474\ttraining's auc: 0.841749\tvalid_1's binary_logloss: 0.241588\tvalid_1's auc: 0.782438\n",
      "[800]\ttraining's binary_logloss: 0.210906\ttraining's auc: 0.855162\tvalid_1's binary_logloss: 0.240953\tvalid_1's auc: 0.784171\n",
      "[1000]\ttraining's binary_logloss: 0.205622\ttraining's auc: 0.867474\tvalid_1's binary_logloss: 0.240699\tvalid_1's auc: 0.78471\n",
      "[1200]\ttraining's binary_logloss: 0.201178\ttraining's auc: 0.877326\tvalid_1's binary_logloss: 0.240542\tvalid_1's auc: 0.785325\n",
      "[1400]\ttraining's binary_logloss: 0.196791\ttraining's auc: 0.886674\tvalid_1's binary_logloss: 0.240328\tvalid_1's auc: 0.785846\n",
      "[1600]\ttraining's binary_logloss: 0.192669\ttraining's auc: 0.895254\tvalid_1's binary_logloss: 0.240305\tvalid_1's auc: 0.785932\n",
      "Early stopping, best iteration is:\n",
      "[1528]\ttraining's binary_logloss: 0.194195\ttraining's auc: 0.892123\tvalid_1's binary_logloss: 0.240254\tvalid_1's auc: 0.786127\n",
      "best iteration of clf is :  1528\n",
      "Fold 10 AUC : 0.786127\n"
     ]
    }
   ],
   "source": [
    " for n_fold, (train_idx, valid_idx) in enumerate(folds.split(X_train_later[feats], y_train_later)):\n",
    "    train_x, train_y = X_train_later[feats].iloc[train_idx], y_train_later.iloc[train_idx]\n",
    "    valid_x, valid_y = X_train_later[feats].iloc[valid_idx], y_train_later.iloc[valid_idx]\n",
    "    clf = LGBMClassifier(\n",
    "            nthread=4,\n",
    "            n_estimators=10000,\n",
    "            learning_rate=0.02,\n",
    "            num_leaves=34,\n",
    "            colsample_bytree=0.9497036,\n",
    "            subsample=0.8715623,\n",
    "            max_depth=8,\n",
    "            reg_alpha=0.041545473,\n",
    "            reg_lambda=0.0735294,\n",
    "            min_split_gain=0.0222415,\n",
    "            min_child_weight=39.3259775,\n",
    "            silent=-1,\n",
    "            verbose=-1, )\n",
    "    clf.fit(train_x, train_y, eval_set=[(train_x, train_y), (valid_x, valid_y)], \n",
    "            eval_metric= 'auc', verbose= 200, early_stopping_rounds= 200)\n",
    "    print(\"best iteration of clf is : \",clf.best_iteration_)\n",
    "    oof_preds[valid_idx] = clf.predict_proba(valid_x, num_iteration=clf.best_iteration_)[:, 1]\n",
    "    sub_preds += clf.predict_proba(X_test[feats], num_iteration=clf.best_iteration_)[:, 1] / folds.n_splits\n",
    "\n",
    "    fold_importance_df = pd.DataFrame()\n",
    "    fold_importance_df[\"feature\"] = feats\n",
    "    fold_importance_df[\"importance\"] = clf.feature_importances_\n",
    "    fold_importance_df[\"fold\"] = n_fold + 1\n",
    "    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
    "    print('Fold %2d AUC : %.6f' % (n_fold + 1, roc_auc_score(valid_y, oof_preds[valid_idx])))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full AUC score 0.788996\n"
     ]
    }
   ],
   "source": [
    "print('Full AUC score %.6f' % roc_auc_score(y_train_later, oof_preds))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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ROG+bRq5nrKTnJE2U9Kikzcvyv5Tb2jUfr1Ko882y/i1TuD+l14ltde9DCGFJEd+iCu3hk53EJV1F2kahtNP3IOABYCAw1PYHkn4CXChpO6A3cATQH+gFvATsCfwqn38A8FRZe+fYPquYIAngP8DJwM3FPNv75TIDgONt71nFNR1ie5yk75D2u9q5kFe6pkHAGNtvk/bHQtJQYFaxf5Ka2mk9hLCEGXbffUybPXuxtdflwQcXSzsNDQ0MHz58kdUfAU5ob/cDmwJI6gF8FdgeuAkYCmD7DknfBQ4nbUA51Pa7knoBc4FnJPW3PQ44CLiWFAg1ZyLQVdLOtu9qo+t5CPh56UApktqfFPDcL2k52x+0tpG8kecQgLVXXrm11YUQOrBps2fz5qxZi6/BxdnWIhQBTmg3kpYGdidttAmwL3CH7eclvSNpC9uP5byfAI8Ak23/tayqa4CBkt4E5gOvs3CAc5yk0l4G79revpD33/nVVgHObsD/FY6/Crxs+0VJY4E9gBuaqaN8I9LTbY8qFrB9CXAJwOZ9+sSGciHUsdW6d1+s7XXp1WuxtNPQ0LBI648AJ7SH4gf4/cBf8vtBwLn5/TX5+DEA269Luge4pUJ9dwC/A94CRlXI/8wUVYnt+yUh6WstupJPXSWpO9AF2KKQPoh0LeT/HkbzAU5MUYUQPnHydtst1vbq5UF/EeCE9vCZD3BJqwA7AJtIMilQsKRf+NMt7xfk10JsfyRpPPAzoB+wV3mZZgwjrcX5uMbzig4hTXmdAVwAfFNSF+BbwN557ykBq0jqaXtmK9oKIYTQjPgWVego9gdG2O5ju6/tdYCXgW2rPP/3wAl5AW9NbN8JrARsVuu5ZfXMA04BviJpY2AnYKLtdfI19QGuJ03FhRBCWIQiwAkdxSDgxrK064GDqznZ9lO2r2wk+7iyr133rVBmGLB2tZ1toh9zScHW8bT8mrqV9feM1vYrhBCWNPp09D+E0Bn179/f48aNa+9uhBDCYiFpvO3+zZWLEZwQQggh1J1YZBxClSTdCKxblnyC7THt0Z8QQgiNiwAnhCqVnnAcQgih44sAJ4RObt60N3jzT//d3t0IIbSBhh+e0t5dqBuxBieEEEIIdScCnBBCCCHUnQhwQgghhFB3IsAJ7ULS/LKH2Z0oqYuk8ZK2K5S7U9IBkh7O5V6VNK2Zh/Yh6buSJkl6QtKTkvbJ6ZJ0iqTJkp6XdK+kfoXzZpXVM1jS+fn9UElTcrtPSxpUVvZ4Sc/m9iZKOjynj5X0XKHPo5u4Lz/NdT8h6W5JfWq/uyGEEGKRcWgvFTeUlHQU8GdJW5C2b7Dt64Drcv5goL/toxurWNLapL2ltrA9Q1IPYLWc/SNgG2Az23Mk7QLcJKmf7Q+q6Pc5ts+StAEwXtJo2/MkHQnsDHzZ9vuSerHwlgyH2K7maXyP5+ubI+mHwHDgoCrOCyG0k9Pvm8D0OdX8+mhel4cOb5N6GhoaGD58eJvU1VlFgBM6FNsPS3oQGEra0mDnFlSzOjATmJXrnFV6D5wADLA9J+fdmds7hE93Na+mn5MlzSHtYTUVOAnY3vb7OX8G0NjWEU3Ve2/h8F/AoZXKSRoCDAFYa+VetTYTQmhD0+d8wJuz5rZNZbOmtE09IQKc0G66SZpQOD7d9qj8/pfAa8C5tl9oQd0TgbeAlyXdDdxg+2ZJKwDdbb9YVn4caRfyquURpsm2p0rqCfSsUG/RVZJKvwHvsv3zKpr5HnB7pQzblwCXAGzWZ63YbyWEdrTq8su1WV1deq3cJvU0NDS0ST2dWQQ4ob1UnKLKtgNmAJu0pGLb8yXtBmwF7AicI2lL4OxGThHQVJBQzDtO0g+A9YDdqjwfqp+iShVKhwL9ga9Xe04IoX38crvGfpXVLp6D03ZikXHoUCR1J6072QFYTdIeLanHySO2TwcGAt/K00ezJa1XVnwL4On8fq6kZQp5KwPTC8fn2N6QtC5mhKTlmqi3RSTtRFpDtLftD9uizhBCWNJEgBM6ml8D19p+FjiKNPpS0/ivpN55Cqlkc+Df+f2ZwB8kdctldwK2BUbm/H+Q173kMgcCxXUxANi+gTS19e2cdDpwQZ4GQ9IKeZ1MTSR9CbiYFNxMrfX8EEIISUxRhfZSvgbnDmAEsB+wGYDtCZLGkBYG/7aGursCZ0nqDXwATAOOzHl/JC0MniRpPvAmsI/t0vqYY4GLJR1DmnoaYfu+Rto5FRgp6VLgT0AP4FFJ84B5wO8LZYtrcKbb3qmROs/M9VwnCeBV23vXcO0hhBAA2bE+MYTObLM+a3nMiT9s726EENpArMFpnqTxtvs3Vy5GcELo5Lqutmb8UgwhhDIR4IROTdLDwLJlyYfZntQe/amWpJOBA8qSr7M9rD36E0II9SYCnNCp2d66vfvQEjmQiWAmhBAWkQhwQujkPpr6b/5z/g/auxshhA5i7aMvbe8udAjxNfEQQggh1J0IcEIIIYRQdyLACSGEEELdiQAntAtJ8yVNKLxOlNRF0nhJ2xXK3SnpAEkP53KvSppWOK9vI/V/V9IkSU9IelLSPjldkk6RNFnS85LuldSvcN6ssnoGSzo/vx8qaUpu92lJg8rKHi/p2dzeREmH5/Sxkp4r9Hl0E/dlO0mPSfpY0v6139kQQggQi4xD+6m42aako4A/560W9idtK3UdcF3OHwz0t310YxVLWpu0l9MWtmdI6gGslrN/BGwDbGZ7jqRdgJsk9bP9QRX9Psf2WZI2AMZLGm17nqQjgZ2BL9t+X1IvYN/CedVutvkqMBg4voqyIYQlxPB/vsH02R9XVXbpRw6vqe6GhgaGDx/ekm51aBHghA7F9sOSHgSGAgeTgoZarQ7MBGblOmeV3pO2fRhge07OuzO3dwjwlxr6OVnSHNK2D1OBk4Dt88ab2J4BXFlrx22/AiBpQVPl8j5XQwDWWqlHrc2EEDqZ6bM/5q3Z86orPHvKou1MJxEBTmgv5XtRnW57VH7/S+A14FzbL7Sg7onAW8DLku4GbrB9c94Is7vtF8vKjwP6lVfSlDzCNNn2VEk9gZ4V6i0q7kV1l+2f19JeOduXAJcAbPq51WK/lRDq3Krdq/+4XnrF1Wuqu6GhodbudAoR4IT2UnGKKtsOmAFs0pKKbc+XtBuwFbAjaUfyLYGzGzlFQFNBQjHvOEk/ANYDdqvyfKh+iiqEED7jF19ds+qy8RycJBYZhw5FUndgOLADsJqkPVpSj5NHbJ8ODAS+laePZktar6z4FsDT+f1cScsU8lYGpheOz7G9IXAQMELSck3UG0IIoZ1EgBM6ml8D19p+FjiKNPqyXC0VSOqdp5BKNgf+nd+fCfxBUrdcdidgW2Bkzv8HcGjO6wYcCNxb3obtG0hTW9/OSacDF+RpMCStkNfJhBBCaAcxRRXaS/kanDuAEcB+wGYAtidIGkNaGPzbGuruCpwlqTfwATANODLn/ZG0MHiSpPnAm8A+tkvrY44FLpZ0DGnqaYTt+xpp51RgpKRLgT8BPYBHJc0D5gG/L5QtrsGZbnunShVK2gq4MfdxL0m/tV3T+qAQQgggO9YnhtCZbfq51XzbL/ZtvmAIYYlQ72twJI233b+5cjGCE0Int8zqfer+F1oIIdQqApzQqUl6GFi2LPkw25Paoz/VknQycEBZ8nW2h7VHf0IIod5EgBM6Ndtbt3cfWiIHMhHMhBDCIhIBTgid3AdTX+DZC/Zp726EsFhs9KO/tXcXQicRXxMPIYQQQt2JACeEEEIIdScCnBBCCCHUnQhwQqcjab6kCZKekjRR0k8lLVVW5jxJU0rpkvpJer70BOOcdqukgZLWkHRLrutpSbc10XZfSXNz+xMlPShpw5w3QNIt+f1gSdNyuWclHZfTT85pEwrXMUHSMZKGSjq+rL1XJK3adncvhBCWDBHghM5oru3N8xN+dwb2AH5TysxBzX6kHcm3A7D9FHADcHIusy/Q1fY1pCcS32V7M9tfAE5spv0Xc/ubAVcCJzVSblTeUPSrwMmS1rE9LJ+7eeE6Nrf9hxbdiRBCCBXFt6hCp2Z7at7z6VFJQ50ezb098CQwChgEjM3FTwUelzQaOAPYK6evCdxZqPOJGrqwAvBuM318W9ILuZ3Xaqg7hLrwxwfm8s6cBW1SV9eHD2+TegAaGhoYPnx4m9UXOpYIcEKnZ/ulPGqzOvAWKai5GvgbcJqkrrbn2Z6Tp4DuA862PTlXcQEwStLRwN+By22/3kST6+d9tHoCywNNPotH0ueA5YBqAqfjJB1aOO7dSJ1DgCEAvVfqVqlICB3GO3MWMHVWG20LNGtK29QT6l4EOKFeCEDSMqQpq+Nsz8xPOt4FuBXA9s2S3gMuLJ1oe4yk9YDdgN1Jozyb2J7WSFsv5ikmJB0EXJLPLXeQpO2BDYEf2P6gius4x/ZZn1yU9EqlQrYvye2yyedWjA3lQoe28vJLAW00gtOrYszfIg0NDW1WV+h4IsAJnV4OTuYDU0nTTr1Iu4VDGmGZQw5wsgWU/ba1/Q4wkrQ7+C2ktTvXV9H8TcDljeSNsn20pP8H3CrpdttvVn1hIdSJH2/bdqOMG/1oRJvVFepbLDIOnZqk1YCLgPPz+ptBwPdt97XdF1gX2EXS8k3UsUMpX1JPYH3g1Sq7sC3wYlMFbD8E/BU4tso6QwghtFKM4ITOqFteA9MV+JgUPJydg5RdgSNKBW3PlvQAaWRnVCP1bQmcL+ljUtD/Z9uPNtF+aQ2OgI+A71fR5/8BHpN0mu2ZVZQPIYTQCkp/9IYQOqtNPreiR5/w9fbuRgiLRexFFSSNt92/uXIxghNCJ7fc6p+PX/ohhFAmApwQKpD0RdLUV9GHtpv8SngIIYSOIQKcECqwPQnYvL37EUIIoWUiwAmhk5s97QUevXiv5guG0Iytjri5vbsQQpuJr4mHEEIIoe5EgBNCCCGEuhMBTgghhBDqTgQ4IYQQQqg7EeCEDkfSfpIsaaN83Dcf/65QZlVJ8ySdL+lkSRPya37h/THNtDNR0tVlaVdImiJp2UI7r5T148eF8udLGpzfj5XUv5DXV9KT+f0ASbdI+k6hfx9JmpTfXyfpeUndCuffKmlgy+9kCCEsueJbVKEjGgQ8AAwEhua0l4A9gV/l4wOApwBsDwOGAUiaVdrpuymSNiYF+NtJ6m57diF7PvBd4E8VTp0KHCvpYtsf1Xhd2L6cvDlnDpy2tz09H58BnAycImlfoKvta2ptI4SSv/zjA96dXf3T6pf95+FVl21oaGD48OEt6VYIi0UEOKFDkdQD+CqwPWmn7qE5ay7wjKT+tscBBwHXAr1b2NTBpAf5bQzsDRRHcs4FjpN0aYXzpgH/BL4NVMpvjVOBxyWNBs4g7Z9VkaQhwBCAhpXbbqfmUF/enW2mz6phO55ZUxZdZ0JYzCLACR3NvsAdtp+X9I6kLYB3ct41wEBJb5JGWV6n5QHOQcDOwIbA0Swc4LxKGkE6DKj0YJAzgNslXdbCtiuyPUfS8cB9wNm2JzdR9hLgEoCN+6wYG8qFilbqrprKL9ur+v+dGhoaau1OCItVBDihoxlEGkGBFNAMAi7Ix3cAvwPeovGdwZslaStgmu1/S/oPcJmklWy/Wyh2GmkE6dby822/LOkR0ijQQlkVmqsp+LB9s6T3gAtrOS+ESr739eVqKr/VESMWUU9CWPwiwAkdhqRVgB2ATSQZ6EIKEC4EsP2RpPHAz4B+NDGF04xBwEalxcPACsC3gD+XCth+QdIE4MBG6jgNGE0abSl5G1ipcLwyML0F/VuQXyGEEFoovkUVOpL9gRG2+9jua3sd4GVg7UKZ3wMn2H67JQ1IWoq0QHnT3EZfYB9S0FNuGHB8pXpsPws8TVr4XDIWOFRSaV7g28C9LelnCCGE1okAJ3Qkg4Aby9KuB04qHdh+yvaVrWhjO2CK7eJqyvuAL0has1jQ9lPAY03UNYyFg69LgJnAREkTgR7AWa3oawghhBaSHesTQ+jM+vfv73HjxrV3N0IIYbGQNN52/+bKxQhOCCGEEOpOLDIOdUvSyaT1NkXX5QcDhhBCqGMR4IS6VXzCcQghhCVLBDghdHIzp09m7KXfaO9uhHY24AefeWRTCEujoW8nAAAgAElEQVS0WIMTQgghhLoTAU4IIYQQ6k4EOCGEEEKoOxHghDYnab6kCZKekjRR0k/zE4SLZc6TNKWULqmfpOcldSuUuVXSQElrSLol1/W0pNuaaLuvpLm5/dLr8Jz3iqT7y8pPkPRkfj9A0gxJj0t6RtJvCum3VGhrGUnnSnpR0mRJf5O0tpIHJO1eKHugpDvK7k/pdWJOHyvpOUlPSHpW0vmSVqz9JxBCCCEWGYdFYa7tzQEkrQ6MBHoBpYBhKWA/4DXSk4XH2n5K0g3AycApkvYFutq+RtLFwF22z8vnb9pM+y+W2q+gp6R1bL8maeMK+ffb3lNSd2BCpcCm4DSgJ/BftudL+g5wA7A1cCRwnaR7SXtqDQN2K78/FRxie5ykZYDTgb8BX2/qYkP9uOrej3hvTssevnrZ/Ye3qu2GhgaGDx/eqjpC6EgiwAmLlO2pkoYAj0oa6vTo7O2BJ0k7gg8i7eEEcCrwuKTRwBl8upnmmsCdhTqfaEWXrgUOIm2hMAi4GjisQr9n54091wemludLWh74DrCu7fn5nMslfRfYwfbdkm4GTgC6k/bYerHaTuaNRX8BvCBpM9sTy9ofAgwBWGPl2naMDh3Xe3PMOzNb+HT5mVOaLxPCEiQCnLDI2X4pj9qsDrzFp4HF34DTJHW1Pc/2HEnHk/aGOtv25FzFBcAoSUcDfwcut/16E02un3cCL/mx7dLU1GjgClKAsxdwCBUCnLyz+VeA3wGrVWjj88Crtt8vSx9H2un8buC3pL2sPgKKjxXvVta/022PKm8gjwpNBDYCJpblXULa+4oN+/aK/VbqxIrLq/lCjei2Qu9Wtd3Q0NCq80PoaCLACYuLIK1bAfYAjrM9U9LDwC7ArQC2b5b0HnBh6UTbYyStR5ri2Z00yrOJ7WmNtNXUFNU7wLuSBgLPAHPK8r8m6XFgAXBGnjob0Mj1VAosPknPo0CjgFm2PyyUaWqKqlJ9YQlxyPbLtPjcAT8Y0YY9CaHziwAnLHI5OJlPmurZi7QeZ5IkgOVJQUbxKWUL8usTtt8hreUZmdfFbEfaabwlRpFGhQZXyLvf9p5V1PEC0EdST9szC+lbADcXjj9zLdWS1AX4IikQCyGEUIP4FlVYpCStBlwEnJ/X3wwCvm+7r+2+wLrALnlNS2N17FDKl9STtC7m1VZ060ZgODCmpRXYng1cCZydAxHyt7WWB+5pRd/IdXUlLTJ+rZVrjkIIYYkUIzhhUSitMekKfAz8lRQILA/sChxRKpincR4gjex8Zh1KtiVwvqSPSUH5n20/2kT75WtwLrP9h0KbM4H/AcijSNXYUdJ/CscHAL8kreV5XtIC4FlgvxzINaV8Dc4dtk/M76+S9CGwLGm90T7VdjCEEMKn1Pzv4hBCR7Zh316++ORt27sboZ3FXlRhSSFpvO3+zZWLEZwQOrmeq24QH24hhFAmApzQKUn6Imnqq+hD21u3R39CCCF0LBHghE7J9iSg2q9ahxBCWMJEgBNCJzdj+mRu+8se7d2N0EJ7fK/RrdVCCK0QXxMPIYQQQt2JACeEEEIIdScCnBBCCCHUnboKcCTNyv/tK8mSflzIO1/S4Pz+K5IeljRB0jOShkr6Tj6eIOkjSZPy+zMKdfxN0kNlbQ7NG0RW07/5uc6nJE2U9NO8CWWxzHmSppTSJfWT9LykboUyt0oaKGkNSbfkup6W1Ohkfr4ncwvXOCE/eRdJr0i6vlB2f0lXNHVPJA2WNC0fPyvpuHzuLpIeUn6CnqQuucw2jfRraL7eCZKelLR3Wf5ESVcXjofk/Z1KxytIelHSurnPc/LTjov305JWLfsZlF4n5vSxksYVzuuf03YtlJ0l6bn8vuLGP5IG5Pa+V0j7Uk47vpC2tKTpkk4vpHWRNF7SdoW0OyUdUKmtEEIIjaurAKfMVOBYpc0dy10JDMkbHm4CXGv7ctub57TXge3zcekDcEXSPkMrSlq3hX2am+vsB+xM2nTyN6XMHNTsB7xG2msJ208BNwAn5zL7Al1tXwOcCtxlezPbXwBOpGkvlq4xv4of0v0l9SsWbu6eAKNy3leBkyWtY/tO4N9A6QP+x8Cjth9sol/n5HoOAC4rBHcbk/6Nbiepey57KbC2pJ3y8amkJxW/nI9fID/9N9ezPTCl0NbcsntwRiFvdUm7l92DMYV7MA44JB8f3sT1TAIOKhwPpGw3cNIGo88BB5aCQdvzgaOACyR1lTQoJfu6JtoKIYRQQT1/i2oa8E/g26QPxaLVgTfgkw+Vp6uo71ukTRTfIn1gnd508abZnippCPCopKH58f7bA0+StiwYBIzNxU8l7aA9GjiDtK0BwJrAnYU6W7Nn0VnAScAhtZ5o+21JL+T+vAYcBzyQR7uOBr5cZT3PKG3HsCopQD2Y9KybjYG9gattW9IPSZtuDgZ2JG3lUHI1Kbj4X2AA6d/AQkFLE84ETgFur7J8Y14FVpC0Rr6O3YDy0bVBwHnAD4GvAA8B2H5Y0oPAUNL179zKvoTFaPQ9H/H+7NrOueYfTcXKlTU0NDB8+PCazwthSVLPAQ6kYOB2SZeVpZ8DPCdpLHAHcKXtD5qpaxDwW1KAM5pWBjgAtl/Kowyr53oHkT6g/wacJqmr7Xm25+TpjfuAs21PzlVcAIySdDRp36LLbb/eRJPlezT92Pb9+f21wFGSPl/rdUj6HLAc8ES+rjcknUv60D4m7wReTT1bk3benpaTDiJ9wG9ICpSuzvU/IWkMcDewr+2PCtVMBvaRtBLpfv4vCwc45ftAnW67NOX1ELCfpO2B4g7hLTGaNCL1OPAY8GHhOruRArMjgBVzP4tTn78kBYrn2n6hUuU5OB4CsNrKy7Wyq6GtvD8b3p1Z2/Y3786c0nyhEELN6jrAsf2ypEdIfwkX00+VdBVpmuBg0gfMgMbqyX+Jfx54II8gfCxpE9tPtkE3S2tVliFNWR1ne6akh3P/bs19vlnSe8CFhesYI2k90gjB7qRRnk1sTytvJHsxT7VUMp80gvFLqh/BOCgHAxsCPygLEi8AzrB9RRX1HCfpUFJQcVC+x1sB02z/W2mTy8skrWT73UL9u9u+t0J9N5BG2bamsLFnNreJewDw36RRnBOq6HdTriWNxG1ECsyKa5D2BO7Ngev1wK8kHZdHEyFNT84gTZ9WZPsS4BKADfr2ig3lOogVukP+X7pqy6/Qu+Z2Ghoaaj4nhCVNXQc42Wmkv6bvKybafhH4k6RLgWmSVrH9diN1HASsBLycl0usQPoAPaU1HcvByXzSNMZeQC9gUm5jeWAOOcDJFuRX8TreAUaSpmxuIX04Xk/L/JUU4DxVZflRto+W9P+AWyXdbvvN3K8Fkqr94D3H9lllaYOAjSS9ko9XIE0T/jkff+ZeFFxDGjW5Mvejym6A7Xsk/Y40bdRitt+UNI80AnUsCwc4g4CvFq5tFdL05N/zWqPhwA6koG4P2/EkuE5i/x0qLflr2h7fq7hePYTQSvW8yBgA28+S1tjsWUqT9A19+qm3ASnIeK+JagYBu9nua7svac3HwNb0S9JqwEXA+Xn9zSDg+4U21gV2kbR8E3XsUMrP3xxan7T+o0VszyNN3/2kxvMeIgVHx7a07aI8bXcAsGnhfuxDukfV9OdV0qLsC5sr24hhwC9aeG7Rr4ETCiMzSFoB2Bb4XOHafsSn1/Zr0qL3Z0kLjs+RFHNQIYRQo7oPcLJhwNqF48NIa3AmkD6YDyl+CBVJ6gt8DvhXKS1/Y+f9vGYE4BRJ/ym9muhHt/wV46dIa2buBH6bg5RdKYzW2J4NPMCnC4or2RIYJ+kJ0hqOP9t+tIny62vhr0gfU6HMX2jZyN7/AN9R4SvarbAdMMV2cXHCfcAXJK1ZTQW2L86jdOW6ld2DMyqcexufrgNqMdsP2v6/suRvAvfY/rCQ9jdgb0mbkb5FNyyfPwEYQ+uny0IIYYmjNHgQQuisNujby+f96qvt3Y3QQrEXVQi1kTTedv/myi0Ja3BCqGu9Vt0gPiRDCKFMBDhtTNIqpK8vl9uxiUXMbdn+F0nTbkUf2t66UvnFRdLJpHU1RdfZHtYe/WktSbuSpuWKXra9X3v0J4QQwsJiiiqETq5///4eN25c8wVDCKEOxBRVCEuId6dPZvTlu7V3N8JitP937mjvLoTQ4S0p36IKIYQQwhIkApwQQggh1J0IcEIIIYRQdyLACSGEEELdiQAntJqk+aUnNEuaKOmnebuFYpnzJE0ppUvqJ+n5vLN2qcytkgZKWkPSLbmupyU1+pAXSX0lPZnfD5A0Q9Ljkp6TdJ+kPRs7N58zNPdrgqQnJe2d06+QtH9Z2VmFNufmcyZKelDShoU+3FKhnbGS+uf335U0SdITuc19mmszhBBCbeJbVKEtfLJDt6TVSZt/9gJ+k9OWIm1B8BppG4axtp+SdANpz6hTJO0LdLV9jaSLgbtsn5fP37SGvtxve8983ubA/0maa7vSs4lKzrF9lqSNgfvzNTTnxcI1HwGcBHy7uZMkrU265i1sz5DUA1itivZCnbr57o+ZOau2x3XcdO/hVZdtaGhg+PDhtXYrhE4vApzQpmxPlTQEeFTS0LyR6PbAk8Ao0qaSY3PxU4HHJY0GzuDTfbfWJO3TVarziRb2ZYKkU4GjqfzwxfLyz0j6GFi1xqZWAN6tsuzqwExgVm5zVul9LfI9HgKw6iqxF2dnNnOWmTGztnNmzJzSfKEQlnAR4IQ2Z/ulPGqzOvAWKai5mrSp5GmSutqeZ3uOpONJG2mebXtyruICYJSko0mbkl5u+/UWducx4OfVFMybpy6guo0218+btfYElgeqfVL0RNI9eVnS3cANtm8u5J8p6ZTmKrF9CXAJwPp9e8XTOjuxnj0E1PYj7LHCWlWXbWhoqLFHIdSHCHDCoiIAScsAewDH2Z4p6WFgF/LO6bZvlvQecGHpRNtjJK0H7AbsThrl2cR2S3b4VhVljpN0KGlk5SDbllTpE6eYVpyiOogUbDT7tD3b8yXtBmwF7AicI2lL20NzkZ/bHv1J52MNTt3ba8fafw3v/50Ri6AnIdSXWGQc2lwOTuYDU0kf+r2ASZJeAbYljegULcivT9h+x/ZI24cBj5LW7rTEl4Bnmilzju3NbX/N9v057W1gpVIBSSsD0xs5/6Za+ufkEdunAwOBb1V7bgghhOpEgBPalKTVgIuA8/P6m0HA9233td0XWBfYRdLyTdSxQylfUk9gfeDVFvRlU+BXpCmvWo0FDsojUACDgXsbKbst8GKVfeotaYtC0ubAv1vQvxBCCE2IKarQFrrl9ShdgY9Ju5mfnYOUXYEjSgVtz5b0AGlB8ahG6tsSOD8v+F0K+LPtR6vsy9ckPU5aFzMVOKaZb1BVZPsWSVsC4yXNJwUwRxaKlNbgCPgI+H4hb0dJ/ykcF3dR7wqcJak38AFpvU+x3hBCCG0gdhMPoZOL3cRDCEuSancTjymqEEIIIdSdmKIKnYKkL5Kmvoo+tF3V17MlnczCU0UA19ke1hb9CyGE0LFEgBM6BduTSAtyW3r+MCCCmRBCWEJEgBNCJ/f225MZccWu7d2NsBgcPnhMe3chhE4j1uCEEEIIoe5EgBNCCCGEuhMBTgghhBDqTgQ4jZC0nyRL2igf95U0V9IESU9LukjSUjWkj5DUtVD/tpIekfRsfg3J6QMkPVTWl6UlvSVpTUlXSHo51ztB0oO5zGBJ0yQ9LmmypDGStqniOo/P7T8paaKkw3P6WEnjCuX657RdC23PkvRcfl9xc5x8PTNyv56R9Juy/PMkTcmbcyKpn6TnJXUrlLlV0sB8jZa0Y4Wf0/6Ffj9X6OPonD5U0hxJqxfOnSVplULZN3NfSsfLUEFu76+F46Xzvb+l7GcxofD6QqH8cZI+kNSr7D5Z0l6FtFskDWj0hxdCCKFRsci4cYOAB0h7BQ3NaS/a3lzS0sA9wL6k3aqbS+8C3AUcCFwlqQEYCexr+zFJqwJjJE0BbgfWltTX9iu53Z2AJ22/IQnKNmQsGGX7aABJ2wM3SNredsW9mCQdCewMfNn2+/kDd99CkdUl7W779lKC7THAmHz+WOB42809Ze5+23tK6g5MkHSL7fE5qNkPeI20l9NY209JugE4GThF0r5AV9vXSBoMTCL9bEpPJx5I2qG76JBG+jQd+BlwQuF63iZ/O0vSUGCW7bOauZ7ZwCaSutmeS7qHU8rKfPKzqGAQaX+t/YArCun/IV33zRXOCUuQO/8+n9mzPvsQ1r/fc3jF8g0NDQwfPnxRdyuETiVGcCqQ1AP4KvA90gfoQmx/DDwIfL7K9PnAI8BaOelHwBW2H8v504FfACfaXgBcBxxUqGIgcHUt12D7XtIO10OaKHYScJTt9/M5M2xfWcg/Ezillnab6dNsYDxpbymA7YEngT+x8AacpwIHSNocOIN0v0ruB74sqWv+OX0emFBlFy4j7S+1csuv4hO3A9/I7wdR5c9H0vpAD9J9Ld90dCIwQ9LOVdQzRNI4SeNmzvyo+l6HTmH2LPP+TD7zmjJlSsXXm2++2d5dDqHDiQCnsn2BO2w/D7yjhTdHRGmPpR1JownVpC8HbA3ckZP6kT7oi8bldEgflgPzucsCewDXF8qeWZj6uKqJ63gM2KhShtImlj1tN7VJ5EPAh3k0qNUkrQJ8BXgqJ5UCgxuBPZWn8GzPAY4H7gOusT25UI2Bv5P2uNqHtJN3uasK9+fMQvosUpBzbBtczjXAwPyz3RR4uCz/oLIpqtKUW+ma7wc2LE6ZZf9NFUGl7Uts97fdv2fPijNpoRPr3kOs0JPPvNZaa62Kr4aGhvbucggdTkxRVTYIODe/vyYfX8CnGywa+Jvt2yX1rSJ9A2C07SdyncplyxnA9qOSekjaENgY+JftdwvlGpuiKqdm8qrZiKz0gXtCcwWbUNoAcwFwRp6GWoYUuB1ne6akh4FdgFsBbN8s6T3gwgr1XQMcA/QiTTmdVJbf2BQVwB9I02S/b8X1YPuJ/DMeBNxWoUhjU1QDgf1sL8hTcQdQ2O3c9v2SkPS11vQvdG677NSlYvrhgysudQshVBABTpk8yrADaY2FgS6kQOBC8pqaCqc1mS5pTWCspL1t30QawejPwqMPWwJPF46vIX0YbkyN01MFXwIqrr/Ja25mS1rP9kuNVWD7Hkm/I428tNT9tvcsS9uNFKBMyuuKlgfmkAOcbEF+lffpEUmbAHNtP5/Pr4rt9ySNBI6q7RIqugk4CxgArNJcYUmbkoLdu3KflwFeohDgZMNIa3E+boM+hhDCEimmqD5rf2CE7T62+9peB3gZWLulFdp+AzgR+GVOugAYnNeYlIKq/wGKqwSvBg4lBVuVpmGaJOnrpPU3lzZR7HTgAkkr5HNWUP42V5lhpDVCbWkQ8P18j/sC6wK75Gm+avySz47cVOts4AhaH+BfBpyat5GoxiBgaOmabfcG1pLUp1jI9p3ASsBmrexfCCEssSLA+axBpDUhRdfT8g/Tkv8Dlpf0tRzwHApcKulZ0sLky2x/8u0Z20+TRjTuyYtzi4prcIpfZy6t+3g+9/dbjX2DKvsTcC/wqKQngX/kNhdi+zZgWouuuoIcxOxKYbQmX+MDwF6NnVfWp9vzQupKimtw/l7h3Omkn/GyNXd+4Xr+Y/u8RrLL1+BsQxqRK/+3dSMVFrKTgsoWB9UhhLCkk13NMowQQke17rq9/NvftGYGMXQWsRdVCCBpvO3+zZWLNTghdHKrrLJBfPCFEEKZCHCWAJIuID3Xp+g825e3YRu7ktYRFb1se7+2amNxyuui7q6QtWN+OGAIIYQOLAKcJYDtHzVfqtVtfPKE43pQfMJxCCGEzicCnBA6uWlvT+biv+7a3t0I2RGH1U2cH0KnFt+iCiGEEELdiQAnhBBCCHUnApwQQggh1J0IcOqMpP0kWdJG+bhvPv5xocz5kgbn91dImpI39UTSqpJeye8HSLqlrP4rJO2f34+V1F/Sw/lhdq9KmlZ4uN1IST8snLu1pCckVVz7JekVSavm9y7uFyXpeElDC8eHS3pS0lOSnpZ0fE6XpFMkTZb0vKR7JfUra+P+snYn5Acdlq55RtlD+nZq4n7PL50v6brSk5gL6aXXiYV79pykiZIeLT3NOuf1kHSxpBfzdd0naevG2g4hhNC4WGRcfwaRngg8EBia06YCx0q62PZHFc6ZD3yX9GTjmtneGiAHTf1Lm0xKWgN4SNJo4G3gfOAo29XssfQh8E1Jp+cnD39C0u7AT4BdbL+utKP3YTn7R8A2wGa250jaBbhJUj/bH+QyPSWtY/s1SRtXaLvS3lmNmVvah0xpZ/cjSVtBfJJewSG2x0n6DnAmsHNO/zNpW5AN8mac65H2Igvt5B93zmf2rNoehvrPuw5vUVsNDQ0MHz68+YIhhKpEgFNHJPUgPe9me9L+VUNz1jTgn8C3qbw31bnAcZKa2reqZrbfknQWaY+tR4EnbD9Q5ekfA5cAx5E2niz6JXC87ddzOx/w6XWdAAywPSfn3SnpQeAQ4C+5zLXAQaSNMgeR9v06jNa7H9i0hvIPAT8HkLQ+sDUp+FmQ+/4SaTPOz8h7hg0BWHmV5VrR5dCU2bPMrPdrO2fW+1MWTWdCCDWJAKe+7AvckXfYfkfSFsA7Oe8M4HZJl1U471XSqM9hwM0V8lvjIlJgNYC0g3otLgCekFT+Z+0mwPjywkqbhna3/WJZ1jigX+F4NHAFKcDZixT8FAOcr0maUDj+VoU6y9teGtgduCMndSur43Tbo8pO2420Rxm5fxNsz2+qnRLbl5ACQPqs2yv2W1lEuvcQUNvt7dVzrRa11dDQ0KLzQgiVRYBTXwaRRmMArsnHFwDYflnSI8DBjZx7GmnU59ZCWmO/2av+jZ+nWi4mTV3V9ARg2+9LGgEcA8yt5dwy5Z9S7wDvShoIPMNnNxitZYqqGMjcz6ejRE1NUV0lqTvQBdiiynZCO/j6Ll1qPueIw0Ysgp6EEGoVAU6dyFsL7ABsIsmkD08DFxaKnUYavbiv/HzbL+QP6gMLyW8DK5UVXRmYTm0W5FdLnAs8BhS3lXgK2BK4p1gwB0SzJa2Xp3dKtiDtlF40ihT8DW5hv0qaCmQacwgwkTSqdgHwTdI1bSZpqdIUVQghhJaLb1HVj/2BEbb72O5rex3SgtW1SwVsPws8DTQ2OjEMOL5wPBnoXVqIK6kPsBkwocK5i4Ttd0hrZr5XSD4dGC6pIfdrWUnH5LwzgT9I6pbzdgK2BUaWVX0jaW1Quzx21vY84BTgK5I2zlNg44DfShKApA0k7dMe/QshhM4uRnDqxyDSiEDR9cBJZWnDgMcrVWD7KUmPkadNbH8o6VDg8vxNpXnA923PaNOeN+/3wNGFft6Wv6H19xwMGCitLfojadRpkqT5wJvAPrYXmuL6/+zde5hWVd3/8fdH84CCYCqOWomCqYmKSmWmJVoe0lQ8Mlqkv6eoHs3CR7NSC03T0CJ7pIOWIl4eMMXzKU88YlmJCiqaImIWlYcUBEVR/Pz+WOvGzc19nBlgZvi+rmsu73vttddaew+X+ztrr72/tueRk4PmeKKofA3OmbavaXLM5Wtwbrf9nbIxLMiPwp9ICuC+nI/1GUlvkGbQTmqy3xBCCIDsWJ8YQle26Wa9/b0zdl7RwwhZ5KIKYdmS9JDtug+txAxOCF3cButtERfVEEIoEwFOWO4k/RlYo6z4i7YfWxHjqScv4L67wqY9m30yLIQQwvIRAU5Y7kpvPu4qchDT7JNSIYQQVqAIcELo4l54ZQY/vWLvFT2MUMcJR8ZtxBCWp3hMPIQQQgjdTgQ4IYQQQuh2IsAJIYQQQrcTAU4nImmoJEvaqlD2YUm3SnpG0pOSrpZ0hKSp+We+pKfy5/GSdpd0s6R+kv4haZWyPqZK+pikUZJmF9qZKqlPlXHtLmmupEdyX/dJWuptyJKmSbqy8H2EpAmF7+tImilpM0k7S/pz7vdJSaPqnJt9JU3Jdf+as5RTdhxPSGot7DNO0qzC8f0xlx8t6aV8PDMk3SFpl7L9DpV0Xd7vmXz8pXZ2WXqEIGmSpOdLbyLOZddLml9Wb6SkNyX1LpQdLOnuwvddc1+xTi6EENogApzOpZWU1XsYQH578C3AL20PsL018Etguu1BOQfSFOCo/H14qSHbzwF/B3YrleXAqZftv+SiMaV28s+cGmObbHsH21uSkl9eIGnPQttbk/49fUopkSTARcAHcroEgDOAi23PAi4FRuRjGEhKx1CRpIHABcAX8jkYCBRzTY3J7RwI/FrSaoVtJxWOrxiYTMjHswXpDdATSykpSmwPze1+OR9/qZ0/1jhPc4BP5nH3ATaqUKcVeBAYWuhrIvCmpCNzUPML4L9tv1OjrxBCCFXEX4edhKSepAvjEFJW71GkzN8P2L6pVM/2vU00eyUpWColmhyWy9rF9lRJZ5DSJ5RmHY4ELgO2Bg4ArrRtSV8HrpB0NLAnKUkmQF/gX7m9RaQcWdV8Gzgr59IiX/R/UV7J9oyc4mBd4MUmjudeSRcCI4CRje5XxVWk83w/KYnmRGCb0kZJ/YGepBQM3wPGFfb9BnBXrv9gnUAqdDJ/vm0RC+ZXfzP81NuHV90G0NLSwujRozt6WCGstCLA6TwOIuUrelrSK5J2JM1UPNSONq8GHpH0jRwUHAEcVtg+MueaAnjV9pAm2n6YJfMkHQF8FtiSFPhcCWD7UUl3kAKhg2wvzPXHAE9JmgTcDlxq+80qfQ0k5WiqKZ+zGbaLwc25kk7Nn6fbPqrG8Xy1Xh8NuBu4SNKqpEBnBHBaYXsr6dxMBraU1Lc0XtvP5lt6xwH9a3UiaURum3XXX7MDhh3aa8F88/pr1be//trs5TeYEEIEOJ1IK/Cz/Pmq/L1dbP9b0nRgT0kvAG/bfrxQZYzt89rYfHGdyVA0av4AACAASURBVEeBl2z/TdI/gIslrWv71VxlLLBvcfbJ9hmSLgf2Is3+tAK7t3EsIyV9Bdgc2Kds20kNJspcKuNmGy0izd4cAfSw/ZyWTOY5DBhq+11JE0kB51iAvF7qM8B8YFPg5Wqd2L4QuBDgg5v3joRynUCPnqW8r5X16bVJzf1bWlo6eEQhrNwiwOkElFIB7AEMlGRgVdL/KU8HPt3O5ku3qV6gA25PFewAPJk/twJbSXouf18HOAT4Tf7+bv5Zgu2ZwC8lXQS8JGm9KqkPppNubU2rMpYxts+TdDAwXlL/GrNBjRxPe10FXEe6zbiYpO2ALYA7c9CzOmkt0dhc5VjgcdKMz1hJn3Bkw+0yPr7vqjW3n3Dk+OU0khACxCLjzuJQYLztTW33s/1BYBbwNLCLpP1KFSXtI2nbJtq+FvgcaUbhqo4YbL5Qly7Cq5BmIbbLY+9HWuxbcwZK0n6Fp422IM18VFvkfC7wPUkfzvuuIumE8kp5oe4U4EtNHs+nSbd7LmpmvxomA2ezdEDZCowqnSfbGwObSNpUUgtwAvBt27cDs0mLm0MIIbRBzOB0Dq2kJ3mKriXdutkf+JmknwFvA48C32y0YdtzJP0J2DA/vVRUXIMDaY3Mc1Wa2k3SI8BapAW8x9u+W9LuwGzbxQUG9wEfkbSR7X9Vae+LwJi8KPgd0pNgi6ocw6OSvgVcKWkt0uzWLVXaPYO0qLkUrBTX4AB8LP/3CEm75uOZBRxiu0NmcPKsS6Vbf8OAfcvKrsvl2wOjbb+Uy78FTJZ0re1XOmJcIYSwMlHMgIfQtX1w894eeebOK3oYoY7IRRVCx5D0kO3B9erFDE4IXdyG798iLp4hhFAmApywmKS9gR+XFc+yPbRS/WXQ/zEsffvtD7aPXR79N0rSdcBmZcUn244oI4QQOom4RRVCFzd48GBPmTJlRQ8jhBCWi7hFFcJK4p+vzmDU1Xuv6GGEMqMOjwm9EFakeEw8hBBCCN1OBDghhBBC6HYiwAkhhBBCtxMBTgghhBC6nQhwuilJiyRNlTRd0jRJJ+S0CsU650uaXSqXtI2kpyX1KNS5RdIwSRtKujm39YSkW2v03U/Sgtx/6Wd43vacpMll9adKejx/3l3SXEmPSHpS0g8K5TdX6Gt1ST+TNFPSDEk3SPqAkvsl7Vuoe7ik28vOT+nnO7l8kqSnJD0q6a+SLpDUp8Fz/bik3+W3LTfSxzRJD0oaVGirp6Rf5+OZLuk+SR+v1X8IIYSlxVNU3dcC24MAJPUFrgB6A6WAYRVgKPB34FPAJNvTc4brU4BTJR0ErGb7Kkm/Bu60fX7ef7s6/c8s9V9BL0kftP13SVtX2D7Z9v6S1gamVgpsCn4E9AI+bHtRfpfORODjwNeA30m6l5TA9Czeyza+oMb4jrI9RdLqpJxSN1A76WnxXF+e+/1pg30cQ8q19dlc/htS6ogtcsbxzYFK5ygsJ4/dsoi35jX/Oo3hNw9vqn5LSwujR49uup8QQmUR4KwEbL8oaQTwoKRROVfSEFLm6gmkXFiTcvUzgEckXUPKj/X5XL4R8PtCm4+2Y0hXk5J/npf7vpKUm6p83K9LegjoT8p/tYQ8U3IMsFkpj5XtSyT9P2CPnCvrJuBkYG1SQtOZjQ7S9kJJ3waekbS97WrZzIsmA/WCv6IHgJPy8fQnBWZH2X43j+FZUsbxJeTf5wiA3uuv2UR3oVlvzTML5ja/3+y5s+tXCiEsMxHgrCRsP5tnbfoCL/BeYHED8CNJq9l+2/Ybkk4kJcz8qe0ZuYmxwARJxwF3AZfY/meNLvtLmlr4/g3bpVtT1wDjSAHO54GjqBDgSFoP2Bn4IbBBhT4GAM/bfq2sfAqwDXA3cDrwMLAQKL4YqkfZ+M62PaG8gzwrNA3YCqgZ4Eh6HymZ5u1N9LEPcH3+vA0wtVrS0bJxXQhcCLBx/97xts5laI1eIuV3bc77e27SVP2Wlpam+wghVBcBzspFkNatAJ8DRtqeJ+nPwF7kDN22b5I0B/hFaUfbd+TbJfuQLuKPSBpYyH5drtYtqleAVyUNA54E3ijbXspc/i5wTr51tnuV46l05VlcnmeBJgDzbb9VqFPr9lGl9mopBjKTgd820Mfl+RbcqsCODY4jrADb7rdqm/Ybdfj4Dh5JCKEZEeCsJHJwsoh0q+fzpPU4j0kCWIsUZNxS2OXd/LOY7VdIa3muyOtiPgVc28YhTSDNCh1dYdtk2/s30MYzwKaSetmeVyjfEbip8H2pY2mUpFWBbUmBWDXNBEslR5FmhM4hnYeDgenA9pJWKd2iCiGE0DbxFNVKQNIGwK+AC/L6m1bgy7b72e5HShy5V+npnypt7FF4OqgXaV3M8+0Y1nXAaKDN77O3/TpwKfDTHIiQn9ZaC7inHWMjt7UaaZHx39u55qgi228DpwI7S9o6rw+aApyuHHlK2kLSgR3ddwghdHcR4HRfPfKjydNJa2Z+T7pwrgXsTWG2JgcK9/PeguJKdgKmSHqUtDD2N7YfrFG/f9kj0scXN9qeZ/vHthc2cUx7SvpH4ecTwHeBN4GnJc0ADgOGun4W2R5l4zunsO3yfJyPkxYntzXAqNUHALYXAD8BTsxFXwZaSAubHwMuAmqtdQohhFBBZBMPoYuLbOIhhJWJGswmHjM4IYQQQuh2YpFxaDNJ2wKXlRW/ZbvbvXk3P7J+d4VNe9r+z/IeTwghhNoiwAltZvsxoNmnh7qkHMSsFMcaQgjdQQQ4IXRxz8+ZwbET96lfMSxzYw++vX6lEMJyEWtwQgghhNDtRIATQgghhG4nApwQQgghdDsR4AQkLSq9FFDSNEkn5MScxTrnS5pdKpe0jaSnJfUo1LlF0jBJG0q6Obf1hKRba/TdT9KC3P8TksbnNwgjaXdJc8telveZvG1DSVdIelbSQ5IekDS0sN/NhXpLjEXStoX2XpE0K3++q9Z4apyLYwrtLZT0WOnFfpKOlnRBYd8Rkv6af/4iadfCtkmSphS+D5Y0qQ2/0hBCWOnFIuMAhVxKkvqS8k31Bn6Qy1YBhgJ/J+WfmpQTYE4ETgFOlXQQsJrtqyT9GrjT9vl5/+3q9D/T9qCcbuFO4HDg8rxtqbxUOY3B9cClto/MZZsCB1Ro+4zysRSf/pI0DrjZ9jX5e79a46lyLi4BLsnbnwOG2H45fz+6MO79ga8Cu9p+WdKOwPWSPmb737laX0n72r6tzjkLy9jzN77D26819yLU4dcPb7qflpYWRo8e3fR+IYTaIsAJS7D9oqQRwIOSRuWUB0NIaQsmkPJYTcrVzyBlFb+GlDSylOphI1JqiFKbDeVxsr1I0l+ATepU3QNYaPtXhX3/BvxvhbptGkuN8VQ7F404GTipFPzYfljSpcCxwGm5zrmk/FQ1A5z8OxoB0HP9NZsYQmjU26+Zt+c2t8/subOXzWBCCE2LACcsxfazeaaiL/AC6UJ+JXAD8CNJq9l+2/Ybkk4E7gN+antGbmIsMEHScaQ8WJfYrptPSdKawMeBbxaKd5M0tfD9EGAb4OEGD6dNY6kxnornosGxbAM8VFY2BfhS4fsDwFBJQ4B5VGH7QuBCgL4Deke+lWVgtXUENHdq+/asF5svraWlpel9Qgj1RYATqills14d+Bww0vY8SX8G9iIn67R9k6Q5wC9KO9q+Q9LmwD7AvqRZnoG2X6rSV/8cxGwBXFM2y1LpFhVl38cCu5JmdT5a3NaGsVQdT71z0UaVrqJnkmZxTm5Hu6GdPnRA8/97HHvw+GUwkhBCW8Qi47CUHBAsAl4kBQa9gcfy+pJdSbMYRe/mn8Vsv2L7CttfBB4krVepZmZeAzQA2FlSpbU0RdOBHQt9HQvsCWxQqXKTY6k1nkbORS1PkLKyF+2Yy4vjvQdYE9i5ibZDCCEURIATliBpA+BXwAV5/U0r8GXb/Wz3AzYD9pK0Vo029ihtl9QL6A88X69v2/8CvgN8t07Ve4A1JX29UFZxPG0dS5XxNH0uyowGfqyU1wpJg4CjKcx+FZwFfLvBdkMIIZSJACcA9MiPNU8nrVP5PXB6vnDvTeEWjO3Xgft5b0FxJTsBUyQ9SlpT8hvbDzY4luuBtSTtlr/vVvaY+KE58DoI+HR+xPsvwKVUvqXTnrEUx/Np2nYuFrN9I3Ax8EdJfwUuAr6QA6nyurcCtW6jhRBCqEHpWhFC6Kr6Dujtw0Z/YkUPIxC5qEJYHiQ9ZHtwvXqxyDiELu5DfbaIC2sIIZSJACcsF5K2BS4rK37L9sdXxHhCCCF0bxHghOWi+PbgEEIIYVmLACeELm7GnJnse8MhK3oYoYbbDrx2RQ8hhJVOPEUVQgghhG4nApwQQgghdDsR4IQQQgih24kAJzRM0vz8336SLOkbhW0XSDo6f95Z0p/zi/melDRK0jGFl/UtlPRY/nxOoY0bJD1Q1ueonNCzkfEtKr2wUNI0SSfkpKFI2l3SXEmP5DH9oEL5U5Luk7R/nX5G5eMfUCgbmcsG5+/PFY5xqqSfF+q+T9LLks4ua3eSpCmF74MlTWrk2EMIISwpFhmHtnoR+KakX9teWLbtUuBw29MkrQpsafsJ4BJIF39giO2XSztI6kPKyzRf0ma2Z7VhTAtyDikk9QWuIOWO+kHePtn2/pLWBqZKurlYnvcbBFwvaYHtu2v09RgwjJQYE+BQynJKlR9jwV7AU8Dhkr7nJd+22VfSvrZva+iIQwghVBQBTmirl4A/AF8ipRwo6gv8C8D2Ipa+8FdyCHAT8AIpcDi7dvXabL8oaQTwoKRRZdtel/QQKS/Vi2Xbpko6AzgOqBXgXA8cCJyZk5POBd5ucHitwPnA10kJNYuzVueSMolHgNOFvH3dfDzv3arbh187vGJ5S0sLo0ePXlbDCmGlFgFOaI9zgNskXVxWPgZ4Kt9euR241PabddpqBU4nBTjX0M4AB8D2s/kWVd9ieU52uTPwQypnIH8YOKlO868Bf5c0kBToTACOKatzr6RF+fOltsdI6kHKfP5VoA/puIsBzgPAUElDgHnVOs/B2wiANTfoUWeoYVnzvHdhTvUAZ/ac2ctxNCEEiAAntIPtUqLLI8vKz5B0OelWzJGki/ju1dqRtCEwALjftiW9I2mg7cc7YJgqfN5N0iPAu8A5tqdLqjQuVSir5CrSbNPepKClPMCpdItqf+Be229IuhY4TdLIPNNVciZpFqdS8lAAbF8IXAjQe8C6kVBuBVOvVaj1S9hk7Y0qlre0tCybAYUQIsAJ7fYj0ozLfcVC2zOBX0q6CHhJ0nq2/1OljSOAdYFZkgDWIQUOp7ZnYPnW0SLSbaitKay1qWMH4MkG6t1EuqU0xfZreez1tAKfzOuQANYDhpCyuANg+x5JPyTNMoUuYLWhPWtuH3/g+OU0khBCSTxFFdrF9l9Ja2wWBw6S9tN7V/stSEHGnBrNtAL72O5nux+wEynAaTNJGwC/Ai4oW8Rbb7/tgNOAsfXq2l5AmmU5q8G21wF2BT5UONZjScdf7izg2w0OO4QQQpmYwQkd4SzgkcL3LwJjJL0BvAMcVXYLZjFJ/YAPAX8qleVbX69JKiXiPFXStwrbP1BlHD0kTQVWy/1eBvy0gfGXbl2tRZrtOb7OE1SL2b6qxubiGpxHgXuAe2y/VahzAzBa0hpl7d4q6aVGxhBCCGFpauKP2xBCJ9R7wLre5Sd7rOhhhBoiF1UIHUfSQ7YH16sXMzghdHFb9OkfF9AQQigTAU7oUvIj3pVuH+1ZYxFzW/s6BTisrPh3thtacxNCCGHFiQAndCk5iBm0nPo6iwYXEIcQQuhcIsAJoYubMecffO76/1nRw1jp3XrQT1b0EEIIBfGYeAghhBC6nQhwQgghhNDtRIATQgghhG4nApwQQgghdDsR4IQuR9JQSZa0Vf7eL3//YaHO+pLelnSBpFMkTc0/iwqfj6/Rx3BJj0uaLukJSSfm8nGSZuX9p0nas7DPJElPFdq/JpePkjQ7l82QNFHSR8r2Gyzpz7nO85JeKrTTr+PPYgghdG/xFFXoilqB+0n5qkblsmdJ+bBOy98PA6bDko97S5pvu+Zj5pL2Bb4F7GX7n5LWJKWfKDnJ9jWShpAyem9R2HaU7SkVmh1j+7zc/hHAPZK2tb04HYPtj+ftRwODbR9X8yyE5Wbh9TNh3sKadYZPHN5QWy0tLYwePbojhhVCqCECnNClSOoJfJKUgftG3gtwFgBPShqcA4wjgKuBjdvQzXeBE23/E8D2m8BFFeo9AGzSbOO2J0jaDzgSOL8N40PSCGAEwJob9GpLE6EZ8xbiOW/VrDJ7zuzlNJgQQiMiwAldzUHA7baflvSKpB2BV/K2q4Bhkv5NymD+T9oW4AwEHmqg3j7A9WVll0takD/fafukKvs+DGzVhrEBYPtC0uwRvQe0REK5Za3X6qhOlY3XXr+hplpaWto/nhBCXRHghK6mFfhZ/nxV/j42f78d+CHwAjBhGY7hXEmjgb7AzmXbqt2iKlfvehk6kdUP6l+3zvh40V8InUosMg5dRs5DtQfwG0nPASeRbkUJwPZC0szL/wDtyT45HdipxvaTgAHAqcClbexjB+DJNu4bQgihjghwQldyKDDe9qa2+9n+IDAL+EChzk+Ak9uZePNsYLSkFgBJa5Q/cWX7XdL6mVUk7d1M45IOAfYCrmzHGEMIIdQQt6hCV9IKnFNWdi3wvdIX29PJT0+1le1bJW0I3CVJgIGLK9SzpDOBbwN35OLiGpyXbX8mfx4p6QvA2sDjwB7FJ6hCCCF0LNmxPjGErmzw4MGeMqWRZT8hhND1SXrI9uB69eIWVQghhBC6nbhFFVZakk4hvRCw6Hf5xYAhhBC6sAhwwkqr+IbjEEII3UsEOCF0cTPm/IvPXXfmih7GSufWoaeu6CGEEGqINTghhBBC6HYiwAkhhBBCtxMBTgghhBC6nQhwQrtJmp//20+SJX2jsO0CSUfnzztL+rOkqZKelDRK0jH5+1RJCyU9lj+fU2jjBkkPlPU5StKJDY5vUW5zuqRpkk6QtEretrukuXn7o5LuktQ3bzta0kuSHpE0Q9Idknap09dSx1htvJKek7R+/mxJlxW2vS/3fXMjxxhCCGFJscg4dLQXgW9K+nXODVV0KXC47WmSVgW2tP0EcAmkCz4wxPbLpR0k9QF2BOZL2sz2rDaMaYHtQbm9vsAVQG/gB3n7ZNv75+1nA8cWtk2wfVzeNgSYKGmI7Wp5pJY6xgbH+DowUFIP2wuAzwKzmzrK0CEW3jAVXnuzbr3h1w2vub2lpYXRo0d31LBCCE2KACd0tJeAPwBfAi4q29YX+BeA7UXAEw20dwhwEylD+DBSnqg2s/2ipBHAg6XZlZKclqEX8EyVfe+VdCEwAhhZpYu2HGPJbcB+wDWktBRXArtVqpiPYQTAmhv0bqKLUNdrb+K5C+pWmz034s8QOrMIcMKycA5wm6Ty/E1jgKckTQJuBy61Xe9P5VbgdFKAcw3tDHAAbD+bb1H1zUW7SZoKrEeaSfle1Z3hYeCrNba35RhLrgK+n29LbUfKf1UxwLF9IXAhQO8Bm0S+lY60zpopPX0dG/d8f83tLS0tHTOeEEKbRIATOpztWZL+AhxZVn6GpMtJmbSPJAUvu1drJye8HADcnxNbviNpoO3HO2CYxWtY8RbVycBo4GsN7LeUGsdYLQhZXG77UUn98j631j2CsEysfuCghuqNj/fghNCpxSLjsKz8CDiZsn9jtmfa/iWwJ7C9pPVqtHEEsC4wK6/P6Ue6TdUukjYHFpHWC5W7EfhUjd13AKqtvwGqHuN/SMdS1AuYU6H/80i3p0IIIbRRBDhhmbD9V9L6k/1LZZL2y+tcALYgBRnlF/iiVmAf2/1s9wN2op0BjqQNgF8BF9iuNKuyKzCzyr6fJq17KV9bVKxT7RjvAw6Q1CvXOxiYltfpFF0MnGH7scaPKoQQQrm4RRWWpbOARwrfvwiMkfQG8A5wVIULPJAeOQc+BPypVJZvfb0m6eO56FRJ3yps/0CVcfTIa2xWy/1eBvy0sL20BkfAXODLhW1HSNoVWAuYBRxS4wmqWsf4qKQLgPslmTR79OXynW3/Azi/RvshhBAaoMp/xIYQuoreAzbxJ8/9+ooexkonclGFsGJIesj24Hr1YgYnhC5uiz4bxcU2hBDKRIATuoW8kPfuCpv2tP2fZdDfWOCTZcXn276ko/sKIYTQvAhwQreQg5jGnu/tmP6OXV59hRBCaF4EOCF0cTPmvMB+E8es6GF0ebccXO3l1CGErigeEw8hhBBCtxMBTgghhBC6nQhwQgghhNDtdIkAR1KLpKskzZT0hKRbJX1Y0lI5iZScKmmGpKcl3Stpm8L2/yfpMUmPSnpc0oG5fJykWZKm5p8/5vKjJb0k6ZHc5h2Sdqkz3lJb0/IYxkvapKzODpIsae/CuO+XtG+hzuGSbs+fT5E0PY97auFld5X6X03SOXm8j0v6S6ldSc8Vjv//JG1a2G9R4finSvpOLp8k6am8z18lXSCpT2G/+ZK2Lez3SuFc3lVljP0kLSjrb3hhjNcW6h4qaVwjv482nvv1CmP4t6TZhe+rVzsvVY5rkqTnpcVvM0bS9ZLml9UbKelNSb0LZQdLurvwfdfcX6yVCyGEJnX6/3HmC8V1pKzMw3LZIGDDKrscC+wCbG/7DUl7ATfmIGd94BRgR9tzJfUENijse5Ltayq0OcH2cbnvIcBESUPqvNH2JNvX5PF/C7hXKVHkwry9Fbg///eOnEzya8DvJN0LrEp6E/A+kj5BSnmwo+23JK0PrF6j7x8CGwEDc/0NgU8Xtg+x/bKk04FTga/k8gW2qz2JdJTtKZJWJ2X0vqHYZk4tMCifo3HAzVXOZdHMGv0NlrSN7ekVttX7fTR77hc/gSVpFDDf9nmlziTVOi+VzCE9Qn5/DgQ3qlCnFXgQGAqMA7A9UdJ/SToSuBr4BfA12+800XcAFt74J/zagqb2GX79I/UrFbS0tDB69Oim9gkhLD+dPsABhgBv2/5VqcD2VKVX+VdyMrC77Tdy3d8rzcYcRUobMA+Yn7fNL31ulO17JV1IyklU97GLnO9ojKShwL7ADfnCeyjwWWCypDVtv2n7cUk35WNYGxhve6ak7YGXbb+V23y5Wn+S1iIFLJsV6r9AumCWewA4vuGDT20tlPRt4BlJ29ue1sz+TTgP+B7p91ZrPFV/H82c+w4e+1WknFn3AwcDE4HiLGJ/oCdwEukYxxX2/QZwV67/oO0/VupA0gjSMbPm+uU5PINfW4Dnvt7UPrObrB9C6Ny6QoAzEHiokYqS1gHWtl2eLHEK6YIxDniBlJ36bmCi7ZsK9c6VVHol7HTb1S6uDwNfbXD8xX22Is18fBKYlYOXScDnSBdBgNNz3YVA6VXUvwe+L+lp0sVvgu3/q9LPAOB52681MKZ9gOsL30s5m0rOtj2hfCfbiyRNy8fTngCnf1l/37A9OX++GvhvSQMaaKfe76PRc19NQ+el4G7gIkmrkgKdEcBphe2tpGzhk4EtJfW1/SKA7WclTQCOA/pX68D2hcCFAL0HfDDyrZTROj2a3mfjnn3qVypoaWlpuo8QwvLTFQKcjiDSH/SLJO0DfBTYk/TX/U62R+V61W5RVWqvLWMoaSX9lU/+7xfJF1nbr+cL3PzCDMx8STsBu5FmtCZI+o7tcW0YB6RbNhuSEj4W3/HfzK2YtpyDcrVuUS0CzgW+C9zWzrE0dO5raPYW1SLS7M0RQA/bzxWW5EAKeobaflfSROAwYCyApFWAz5BmFjcFqs7WhepWP2DnpvcZH+/BCaFb6QqLjKcDOzVSMc9avC5p87JNOwJP5Dq2/RfbZ5MuNIe0YUw7ALXW31TdJ/9VfwhpRuY54H+BfSX1KtR9N/8sZnuR7Um2f0D6677auJ8BPlTWXrkhpIvndOCMJo+DfAzb0vw5aNZlwKdIWcVrqff7aObcd5SrcvtL3BqUtB2wBXBnHsMwUtBVcizwOPBfwFiVRUYhhBAa0xUCnHuANSSVFsIi6aOkC3Ql5wI/l9Qj1/0MsCtwhaSNJe1YqDsI+Fszg5H0adIth4sarC9Jx5MWmt5O+ut8mu0P2u5ne1PgWuCgGm1sKWmLRsad1x79lnQOVs/7byTpC2X1FpAW4A6X9P5GjiW3tRppkfHfbT/a6H5tYfttYAxpnNXGU/X30RHnvh0mk87TlWXlrcCo3H8/2xsDm0jaVFILcALwbdu3A7OBLy+DsYUQQrfX6W9R5aeLhgI/y4/nvgk8R7robSnpH4XqI0l/Na8LPCZpEfBv4EDbCyT1Bc6TtHFu5yXga4X9i2twAD6W/3uEpF2BtYBZwCF1nqAqtXVa3udPpCeXFkpqJT0VVnQt8HXSjEUlPYH/zU/kvEOapRlRo+9TgTOBJyS9CbwOfL+8ku1/SbqSNGvwQ5Zea3K77dIj0ZdLegtYg7QO6MAa/TeqfA3OxbZ/Xlbntyx5Gw3q/z468txD7fNSUV7gfF6FTcNIC56Lrsvl2wOjbb+Uy79FWgh9re1XavUXQghhSUr/Hw4hdFW9B3zQu44+YUUPo8uLXFQhdA2SHrI9uF69Tj+DE0KobYs+G8bFOYQQykSA0w6SxpIeOy463/Yly6n/64DNyopPtn3H8ui/EZK2ZenbP2/Zrvom5q6gK5z7EEJYmUWA0w62j13B/Q9dkf03oviG4+6kK5z7EEJYmUWAE0IXN2POS+w38Zcrehjdyi0Hf31FDyGE0E5d4THxEEIIIYSmRIATQgghhG4nApwQQgghdDsR4HQCkhZJmirpcUm/yxnBy8tvyi/6Q1I/SQvyttLPcEnjJH21rO2DJN1ao+8WSVdJminpCUm3Svpw7sOSvlGoe4GkoyWNzX0+UTaOQ6v0MU7SrFznYUmfKGx7n6SXJZ1dKPuRpB8XEq+P9QAAIABJREFUvm8q6VlJfSRNkvR8MYWBpOslza91bvK25yRdW9jv0Dy2Ywp1F0p6LH8+p8rxHJ3PzZ6FsqG57ND8fZKkpwrtXlPWxrT8ksXy8zRb0hr5+/pK6RxCCCE0KQKczmGB7UG2B5KyiH+tQvkrpDcOl8zM20o/40lpAYaVtT2MpdMFACmVAektupNs97f9EeB7wIa5yovAN5VTPpTYPjYnn/xc2ThqJSo9Ke/zHeDXhfK9gKeAwwtByw+BAyVtnb+fD5xme07+Pof8eH4O+jYq66vSuSkZLGmbsuO5pFQX+CfpzceD6ryt+DGWzCE1jKUzqx9VGMPi4C8f1yrApyStXbbPIuD/1eg3hBBCA+Ipqs5nMrBdhfIHqpQX3QWMk7RRTsOwFin/0leq1B8CvG37V6UC21MhzYSQUln8AfgSDebeasB9wIDC91ZSAPN1YGfggZxW4wTgF5JGA71sX17Y5ypSQHE/cDApG/gSQUsN55GCuKPadRTp97SbUm6uNUjHNLX2LosdSXo30NbAASwZgP4MGCmpo853KFh44334tTfq1ht+/QMNt9nS0sLo0aPbM6wQwjIQAU4nIul9pDxFt5eVrwrsScrLVFKex+kbtidLmggcTgoaDgDutT2vSpcDgYfqDOsc4DZJFzd+JDV9njT7gVJC1D2BrwJ9SMHOAwC2b5X0X8B4UrLUoruBi/J5GUbKy3VaYXvFc5M/Xw38t6RikNUWJgWUewO9gRtZ+sV/l0takD/fafuk/PkI4LPAlqTM8MUA53lS4PZF4KZqnUsaQc5Htub6DedKXen5tTfw3Pl1681uoE4IoXOLAKdzKCZznMx7gUypvB8pELmzsM/MfEul3JWkjOrnky7+4yvUaZjtWZL+Qpp1aI9SItOXgP/KZfuTArA38tqY0ySNtL0obx8L9LD9VFlbi0hBwBF5+3OFJTlQ/dyU9j0X+C5wWzuP6SrgeFKA8z+kmaGio2xPKRZI+ijwku2/KSWKvVjSurZfLVT7ESlguqVax7YvBC4E6D1g00go1yCts1ZD9Tbu2bvhNltaWto6nBDCMhQBTuewoMoFeYHtQZJ6AzeT1uCUZ9su9wdgI0nbA7uw9JqcoulAxYXBZX4EXEO6vdRWJ1VYo9MKfLKwkHY90m2zu/L3d/NPJVeR1g+NasNYLiMFONPbsO9itv8iaSDp9/R0WZBVTSuwVeGY1wEOAX5TaPeZHNge3p7xhaWtfsCnGqo3Pl70F0KXF4uMuwDbc0kzBSfmNR+16pp0G+ZS4Fbbb9aofg+whqTFa3QkfVTSp8va/CvwBGnGpUNIWod06+lDtvvZ7kcK4Fpr7vieycDZVFlAXYvtt4ExwLea3beC77L0zE1FklYBDgO2KxzzgVQ+5rOAEztgfCGEsFKKAKeLsP0I6Smd0oxM/7JHoY8vVL8S2J40y1GrTQNDgc/mx8Snk2ZE/lmh+lnAB9p5GEUHA/fYfqtQdgNwQOkx6VqcnGf75Qqba52bkt/SATOYtm+zfW+VzZcXxnAX8Clgtu3ZhTr3AR+RtMSTYLanAw+3d3whhLCyUrrGhRC6qt4DNvWuo2s90R6aFbmoQui8JD1ke3C9erEGJ4Qubos+G8QFOYQQykSAsxKQtB7p0epye9r+Twf2M5b8Ar6C821f0lF9LE+SjgG+WVb8B9vHVqofQgih84gAZyWQg5hqj013ZD/d6sKfA7MuGZyFEMLKrm6AI2lD0mPCG9veV9JHgE/Y/m2dXUMIy8Ezr77M/tdGHLYs3HzIMSt6CCGENmrkKapxwB3Axvn703TM47UhhBBCCMtEIwHO+ravJr9wzfY7pLfBhhBCCCF0So0EOK/nRaoGkLQzMHeZjiqEEEIIoR0aWWR8AikvTn9JfwA2oLHX+4cQQgghrBA1Z3Dyq+XXBD5Nymv0VWAb248uh7E1TNJQSZa0VaHsw5JulfSMpCclXS3piMKbZedLeip/Hi9pd0k3S+on6R/52It9TJX0MUmjJM0ue1Nunyrj2l3SXEmP5L7uk7RUugNJ0yRdWfg+QtKEwvd18puGN5O0s6Q/536flDSqzrnZV9KUXPevks7L5cXjeEJSa2GfcZJmFY7vj7n8aEkv5eOZIekOSbuU7XeopOvyfs/k4y+1s8vSIwRJkwq/i6mSrimM8Q1JfQt15xc+L8r1p+dzeELp99aOcz+2cE4WFMZ0aLXzUuWYjs7/JvcslJX+nR5aOO7B+fNzSglHS3UPlTSuWvshhBBqqzmDY/tdST+x/QnamZhwGWslZZceBoyStCYpE/MJtm8CkDSElMV5UP4+CTixlO1Z0u4AOTP134HdgP/L27YCeuXkip8Dxtg+r8GxTba9f25nEHC9pAW2785lW5MCzU9JWtv268BFwJckfcb2XcAZwMU5s/ftwOG2p0laFdiyWsdKiSAvAPaz/VdJ7wNGFKqMsX2epC2AhyRdk/M0QeXkmAATbB9XOKcTJQ2x/WSpgu2hhXN6Yun461gq83b2MilT98kVti0o/D77AleQMnv/IG9v+tyXHnWX1A+4uZgENQdI1c5LJY+R/m2W3kE0jJRuo5rBkrbJaRpCB3rrxrvxvPn1K5YZfkO1LBxLa2lpYfTo0U33EUJYNhq5RfV7SYcAE90J8zpI6kl6udwQ0q20UcCRwAOl4AagRr6gSq4kXYz+L38fRhuSOpazPVXSGcBxvHfRO5KU3Xpr4ADgStuW9HXgCklHA3sCO+X6fYF/5fYWkZJgVvNt4KycLLO0QPwXFcY1Q9IbwLrAi00cz72SLiQFTSMb3a9JFwNHS/qx7VdqjOVFSSOAByvNajV67jt47JOB3ZQSpK4BDACm1qh/Hilx51H1Gs7HOgKgx/rrtX+k3Zznzcdz5zW93+w27BNC6BwaXYOzNvCOpDcBkXIdrrNMR9a4g4DbbT8t6RVJOwIDgYfa0ebVwCOSvpGDgiNIWaBLRkr6Qv78qu0hTbT9MHBS4fsRwGdJMzHHkS+yth+VdAfpYnyQ7YW5/hjgqTwDdTtwaY2M4QOBn9QbUD5nM2wXg5tzJZ2aP0+3Xe2i+zDp1mV7XS5pQf58p+3SOZpPCnK+yXszMxXZfjbfoupbpUpD576ORs8LpIX5dwF7k2aWbgQ2q1H/auC/JQ2oNwjbFwIXAvTp36/T/eHR2ahXzzbtt3HPxv8319LS0qY+QgjLRt0Ax3av5TGQdmgFfpY/X5W/t4vtfytl1t5T0gvA27YfL1Rp5hZVOS3+IH2UdNvsb5L+AVwsaV3br+YqY4F9i7NPts+QdDmwF2kGohXYvY1jGSnpK8DmwD5l2xq9FaP6VRpS7RYVwM+BqZLqBmt1xtPMua+mmVtUkP5NHk8KcP6HNENTzSLgXOC7wG1N9BHqWOOAPetXqmB8vOgvhC6rkTcZf6pSue37On44zVF6fH0PYKAkA6uS/mo+nbQwuj1Kt6leoGNvXewAlNartAJbSXouf18HOAT4Tf7+bv5Zgu2ZwC8lXQS8JGm9KjmlppNubVVb91Fag3MwMF5S/xqzQY0czzJhe46kK4D/rlVP0uakIOFF0m2ncs2c+w6R120NJK0XelqqGw9eRgpwYh1OCCG0QyPvwTmp8HMacBNpnUtncCgw3vamtvvZ/iAwi/S25V0k7VeqKGkfSds20fa1wOdItzGu6ojBStqOdA7H5lsphwHb5bH3Aw6kzgyUpP303lVyC9IFfU6V6ucC35P04bzvKpJOKK9keyIwBfhSk8fzadI6kIua2a+Nfkq6FVYxKJe0AfAr4IJKa8U64ty3w3epPXOzWF7kPYZ4W3gIIbRLI7eoPl/8LumDQGd5VKAVOKes7FrSrZv9gZ9J+hnwNvAoS2eGrirPGvwJ2ND2rLLNxTU4kNbIPFelqd0kPQKsRZpZON723fkJo9m2Zxfq3gd8RNJGtv9Vpb0vAmPyouB3SLd2Kr5ZOq/j+RZwpaS1SLNbt1Rp9wzSouZSsFJcawLwsfzfIyTtmo9nFnBI8QmqdiiuwXnZ9mfKjuVlSdex5GLmHpKmAquRzsVlpECopKPPPVQ4L4X1URXZbvZ202+BU+vWCiGEUJWafTAqzx48aruZ2ZAQwjIyePBgT5lSbflSCCF0L5Iesj24Xr1G1uD8LzlNA+mW1iBqv8sjhBBCCGGFauQx8eKfhu+Q3tPyh2U0ni5J0t7Aj8uKZ5VeeLcc+j+GpW+//aH00rrOIt9iKn9M+mTbd6yI8XSErnLuQwhhZVP3FpWkb9o+v15ZCGHFiFtUIYSVSaO3qBoJcB62vWNZ2SO2d2jnGEMIHaBP/829649/uKKH0eXcfGjdF0aHEDqhdq/BUUq+eCSwmaQbC5t6AZXeuRJCCCGE0CnUWoPzR1LOo/VZ8nX/80iPXIcQQgghdEpVAxzbfwP+Bnxi+Q0nhBBCCKH96r7JWNLOkh6UNF/SQkmLJL22PAbX3eRzN1XSdEnTJJ2Q36pbrHO+pNmlcknbSHpaUo9CnVskDZO0oaSbc1tPSLq1Rt/9JD1eZdv7JL0s6eyy8v0lPVJo/6uSTsnHMLVwPFMlHV+l7VH5eKZKelzSAYXyE8vqPidp/fz5A5JukDRD0sx8XlbP23aXZEmfL+x7c36BH5ImSXqqMLaauaMkDc9jm56P88RcPk7SoWV155d9HynpTUm9C2X1xvc+ST/Kx1Ya4ymFusXzOlXSd2qNP4QQwtIaeUz8AlJOpt8Bg4HhQN1sx6GiBbYHAUjqC1xBSsL4g1y2CjAU+DvwKWCS7emSJgKnAKdKOghYzfZVkn5Nyrx9ft5/uzaOay/gKeBwSd+zbUmrkbJVf8z2PyStAfSz/RRwVu5vful46ijlvNoamJyPvar8MsmJwC9tHyhp1TyWs3gvG/g/SOfkpirN1EreWexrX1JahL1s/1PSmqS3RTeqFXiQ9HsbVyivNb4zgRZgW9tvSupFSsRZsqDB8xrKvHXT7Xje/PoVgeE3Nvd2gpaWFkaP7iwvcQ8h1NNIgIPtZyStmlMCXCLpj8t4XN2e7RcljQAelDQq508aAjwOTCBdOCfl6mcAj+SZiHOA0szARsDvC222dW1UK3A+8HVgZ+AB0mLy95EXlNt+ixQEtZntJyW9Q1rXVcsewJu2L8n7LZI0Epgl6Qe5zjRgNUmftX1nO4b1XeBE2//Mfb1Jg7m1JPUHepKCru+xZIBTcXxKKTO+QgoW38x9zqPJ/G75384IgB7rr9fMrt2a583HcxubYJ7dYL0QQtfUSIDzRr41MFXSaNLC47WX7bBWDrafzbM2fUlZy1tJmctvAH4kaTXbb9t+I982uQ/4qe0ZuYmxwARJxwF3AZeULtSNyre+9iQlsuyTx/CA7VeUnp77m6S7gZtJL3lcKrt5E319nJQd/aVcVJ7Ta+P8322Ah4r72n5N0vMsOXt4Zv6pFOAUc1vdafukCnUABpb3VaY891RR6fc1GdhSUl/bL9YZ3wDg+RzUVFPKsVVytu0JxQq2LyTNatGn/+bN5VvpxtSrZ8N1N+7Zq6m2W1pamh1OCGEFaiTA+SJprc5xpESHHwQOWZaDWskIIAeRnwNG2p4n6c+kW0e3ANi+SdIc4BelHW3fIWlzYB9gX9Isz0DbL5V3UsP+wL05iLoWOE3SSNuLbH9ZKQP7Z4ATgc8CR7fhGEuBzDzgiHwLDPKtq8UnQnqu9JH30oMULVFue7IkJO1WoW5Dt6gacJLtxWt4ytbgDAOG2n4330Y8jBR0NjK+UnulNyGvB+xi++/ELao2W+Pz+zRcd3y8ByeEbq2RbOJ/y3/lb2T79OUwppVGDk4WkTJdf560HuexfPFfC3iDJbN/v5t/FrP9CmktzxWSbiat3bm2iWG0Ap8sBBfrkW6V3ZXbfyyP6TJS9vCjm2i7ZIlApgHTKQuiJa1DCq5n5jGWnEVa6/JOG8ZV6msn4J5mdsrrnbYA7sy/r9WBZykEOFXG9wzwIUm9bM/Lt+EuUVoAvmobjyGEEEKZRp6i+jwwFbg9fx+kJV/8F9pA0gbAr4AL8vqbVuDLtvvZ7kfK2bRXXrNRrY09StvzQtX+wPNNjGEdYFfgQ4V+jwVaJfUsPfWTDSK9NmB5uBtYS9LwPM5VSe9iGmf7jWJF278H1gW2b2NfZwOjJbXkvtZQlSfCyrQCo0rnzfbGwCaSNq01vjz+3wIX5AXNpeNbvY3jDyGEUEHdAIe0+PFjwBwA21OBfstuSN1aj/zY73TSDMnvgdNzkLI3hdka268D9/PeguJKdgKmSHqUtDD4N7YfrFF/S0n/KP2Q1t3ckxcQl9wAHECaTfi28uPWwOm0bfamaTngGwocJmkG8DTwJmkhbyVnAR8oK7u88Jj1XTX6upU063JX/r08RGO3bocB15WVXZfL643vFNJatsclPUJaw3MpUFo/1UNLPiZ+TgPjCSGEUNBILqo/2/64CvmnJD1qu62PJIcQOlDkomqbyEUVQtek9uaiKnhc0pHAqpK2AI4npXEIIXQCA9Z9f1ysQwihTK1km5fZ/iJpUec2wFukR2LvAOLPxU4qP/V0WVnxW7Y/voz7PYX0FFHR72yftSz7bVRnH18IIYSOVfUWlaQnSI8e30h6qmYJ+emdEMIKNnjwYE+Z0hFPxIcQQufXEbeofkV6cmpzoPh/z9K7SDZv1whDCB3imVdfZf/f/W5FD6Pbuvmw8om/EEJXUPUpKts/t701cLHtzQs/m9mO4CaEEEIInVbdx8Rtf315DCSEEEIIoaM08h6cEEIIIYQuZZkFOJIW5ZeUTZP0sKRdcvnuOaVAse44SYfmz5Pyy+WmSXpQ0qBCvd6SxkuamX/GS+rdwFhGSnqzWDePw5L+q1C2Qy47UdLYPP4nJC0ovHTt0Br9nCjpr5Iez+MvvYm31jE9J+mxQvs/L5yTWXmfp/OxblK234aF/f4taXbhe8U34+bju6zw/X2SXqrwO7lB0gNlZT+XdFrh+ymSylMTFOtL0qmSZuRjuFfSNoXj+2pZ/YMk3Zo/L9KSL7v7ThPn8rH8eztT0hp5W6P/7kr9XUMVkkbl8zigUDYylw0uG8sSv9fCOX9Z0tll7U6SNKXwfbCkSdXGEUIIobplOYOzwPYg29sD3yW9Er9RR+X9fgGcWyj/LfCs7f62+5NyI/2mgfZagQdJb8ctegw4ovB9GDANwPaxOeHh54CZ+VgGFRMvFkn6GikZ5cdsDyTlhFIDxwQwpNB+MU3ASXmfLYFHgHvLApdFpf1Ii8LHFNpZWOVcvA4MVMovRh7z7LJj6QPsCPSRtFlh06nAMZI2z+VfJr2Vt5pjgV2A7W1/mPRv4EalFAVXsvRbf4flcnjv30/pp/g233rnclvS27c3J2fcbtBRhf6qBrLZY2XjPxR4osJYKv1e9wKeAg6XpLJ9+krat4kxhxBCqKCRF/11hHWAV9uw3wPASQD5r+WdWDIgOQN4RlJ/2zMrNSCpP9Azt/M9YFxh8/PAOpI2JCW83Ae4tQ3jJLc9xPZrALbnkl6/X/WYGpVTF4yRNJT06P4NbRxjyW3AfsA1pODvSqCY8foQ4CbgBdJF/Ow8jteU3idzQa73fdtzavRzMrB7KX+U7d9L+iNwFOn3ME7SRrb/pZSu4jPAV5o4jqrn0vb8HHT+XdL7m2izUdcDBwJnKiVNnQu83eC+rcD5wNeBnUnHUXIuKZC8reOGGip56+ab8bx5desNv+mmunVaWloYPXp0RwwrhNBBlmWA00Mph9GawEbAHm1oYx/ShQTgI8BU24tKG20vyn1sQ3ohYSWlC/hkUi6mvrZfLGy/hvQCuEeAh0kvNGyKUqLLXtWCrDLFYyq5V1LpuC61PabKvg8DW9H+AOcq4Pv5ls12wMUsGeC0knJPvUA6P4tn32xfqZSMcpHt8hcKLqaUyHPtCudkCrBN/t1NBA4nXewPAO61XbrilP79lJxte0JZW5XO5WI5IJtFyvrdiMslLcif77RdKxB9jRQ8DSQFOhOAY8rqLPV7zTNne5LygPUhnetigPMAMFTSEKDq1VfSCGAEQI/112/o4MKSPG8enju3br3ZDdQJIXQ+yzLAWZBvnSDpE8D4fDGolvyqWH65pLVJCR93zGWi8r7VykuGAUNtv5svqIeRkiuWXE26OG1FCoR2qXlUldUbA1Q+ppIhtl9usJ92s/2opH6ki+sSM1Z5NmsAcL9tS3pH0kDbj+ftHwBaAEvqaXt+k90Xz9WVpBmL80m/p/GFeov//VRQ61xW6g8a+3d3lO1m3ph3FWnce5OClvIAp9LvdX9SIPeGpGuB0ySNLAbuwJmkWZyTq3Vs+0Ly7bc+/fvX+7cXKlCvXg3V27hnz7p1Wlpa2jucEEIHWy63qGw/IGl9YAPgP8C6ZVXeDxQvBEeR1sKcQwpGDgamAztIWsX2uwCSVgG2B56s1K+k7Uh/vd+ZlzqsDjxLIcCx/W9Jb5PWonyTNgQ4eabgdUmb2362SrVKx9SsHYC727BfJTcC5wG7A+sVyo8g/X5m5XO2Dukifmrefj4pw/zWwA+ofouo2jnZEfi//PkPwEaStied90qZuCtp6FzmmbV+pGzkm1D/312zbiIFaFPy8TayTyvwSUnP5e/rkd4Uvjjjue17JP2QdPsqLCNr7L9/Q/XGx4v+QuiSlstj4pK2Iv21/R9gBrCxpK3ztk1JQUrxdgS23yZdVHeWtLXtZ0i3kU4tVDsVeDhvq6QVGGW7X/7ZGNgk91n0feDksr+im3U2MDbfmkHSOvk2QtVjarRhJceTbvXd3o4xFl0MnGH7sbLyVmCf0jkjrXsalsexL9CXNNPyQ9KtlI/U6ONc4OelBc2SPgPsClwBi9cWXU1aq3Sr7TcbHXy9cympJ2kR8vW2X6XBf3fNsL2ANMvSUD6r/G9jV+BDhfN7LOmclzsL+HZbxxZCCCu75bEGB9Jtgi/lAGKRpC8A/5+9O4+3a7r7OP75UlTEEENcariaKCUI0uqjtEkp8VRFHjS5tKpTOmiraam2tI2qKWhQOqRKqJIoIbRmpShKSEhijqhSQ0yJEFPyff5Y67Czc865594MN/fm93697ss5a69pn3tfzi97rb1/5+a7ad4Gvpo35S7A9lxJpwKHA1/JP7+W9Fju845cVstQ0qbcosty+b8K4yyO7Oi/JW1mvjtfEXobOLVcqco5wYJ7Ne63fXB+fbLSbdndgDtJSx617o5qE9tPka7GvCsvW22Sx6rUmyFptqRPAqcB++fA5DVJPyRtOK61v+rXpKsmU/L5PQsMyoFBxUWkq0A/KrUt78G5xvYCdep8liIF75eRE8PafrOBv7viHpwXbO9e47yKcxhb5/ACv1fg78DfbRf3eU0ARirfzl7o9ypJM1sbP4QQQnU1k22GEDqHtXr18i4nnth6xdAukYsqhGWLFkOyzRBCJ9C7R4/4Eg4hhJIuEeBI2gYo37L8pu2dlsBYZwEfLxWfbvvcxT1We0lah+qbkXez/eISGG+Z/0zaIz/zpxw5/MV2Q3tuQgghdJxYogqhk+vXr58nTmzL3e0hhNB5xRJVCMuJx16exT6XtP603VDfFft/tqOnEEJYjCKbeAghhBC6nAhwQgghhNDlRIATQgghhC4nApwQQgghdDkR4HQQSU2SxkqaLukBSVdJ+pCkqVXqStLRkh6V9IikmyRtXTj+ZUlTJN0vaaqkQbl8jKQZkibnn9tz+SGSZkqalPu8VlLdHFyFvu7Lczhf0gdKdbaXZEl7FuZ9W07xUKnzOUnX5NdHSZqW5z1ZUs3b+iWtJOnEPN+pku6q9CvpicL5/6OYikPSvML5T5b0o1x+s6SHc5uHJJ0paa1CuzmStim0e6nwWd6w8AzTk6Dz+R9bKFtX0tuSzizVvU/SRaWyKyR9ofD+D5LqZTQPIYRQQ9xF1QFyKoHLgPNsV/I89QXWr9HkUFIyyu1yFuo9gCtykLMucBSwg+1ZSjmY1iu0PcL2JVX6HGf723nsAcB4SQNsV01cWuwrz/97pFQEfQrpI1qA2/J/r83ZyL8B/EXSTaR8ZMcBA5UyzO+d5/2mUjLWleuMfSwpF1efXH994JOF4wNsvyDpGFKOqq/l8npZyQ+yPVHSyqRcYhOKfeY8XX3zZzQG+GuNz7Lo8XxeP83vDyAlin2XUj6sFYBPSFrN9mv50HdJn+mVwFbATsC3WhkvVDH3yvH41dltanPwFX9puG5TUxMjR45s67RCCEtRBDgdYwDwtu3fVQpsT1bKBVXNkUB/26/nutflqzEHkRKQvgrMycfmVF43yvZNkkYDw4DhDdQ3MErSYFKurwk56NmflJX9Vknvt/2G7an5C/tIYDXgfNvTlTKIv1DJy2S7ZlZvSd1IActmhfrPkRJ1lt1BChQaZvstpbxaj0nazvZ9bWlfMhd4UFI/2xNJ2dkvBjYs1DmQ9GDKDwP7kPJxYfuJ/HsYCXwU+HZOKroQpUSuwwBWXXe9alWWa351Np71SpvaPN3G+iGEZVsEOB2jD3BPIxWVMlCvZnt66dBEYGtgDPAcMEPSjcB428WHopwsqZKBfZrtg2oMdS/w9QbnX2yzJenKx8eBGTl4uRn4X2B8rndMrvsWUHk403XAzyQ9AtxAuqL0jxrj9AaetN3IP8kHApcX3peTdp5ge1y5ke15ku7L57MoAQ7AWGCopGeBecB/WTDAGUIKBLcAvk0OcLJTgOnArbZvqTWA7dHAaIC1em0eT+ss0eprtLnNht1Xa7huU1NTm/sPISxdEeB0XiJdTJknaSDwEWA30pWVHW2PyPVqLVFV6689c6hoIX2xk//7BXKAY/s1SeOAOYUrMHMk7QjsSrqiNU7Sj2yPacc8IC3trA88T1qiqqi3RFXvfBbFNaQlteeABYIpSR8BZtr+t6SngHMk9bD9cq6ybZ7HlpJWsD1/Mc1pubLqZ/+vzW3Ojwf9hdCQ86okAAAgAElEQVSlxCbjjjEN2LGRivmqxWuSPlg6tAPwQK5j23fZPgEYCuzXjjltD9Tbf1OzjaQV85g/k/QE8GtgL0mrF+rOzz/vsj3P9s22f066klFr3o8Bm5T6KxsAbEr6bH/RxvMgn8M2tP0zWEjek3QP8APg0tLhFlLw8gTpSs0a5POWtALwG1Jw+CjwzUWdSwghLK8iwOkYfwdWkVTZCFv5l/2mNeqfDJwhadVcd3dgF+BCSRtK2qFQty/w77ZMRtInSfs5/tBgfUn6LmnT7zXA7sB9tje23Wx7U9IX+751+thC0uaNzDvvPfoj6TNYObffQNLnS/XmkjY/Hyxp7UbOJfe1EmmT8X9s399ou1acChxZTG6aA5gDgG3z59QMDCIFPZCWCB+1fTPwfeCHkmKDTQghtEMsUXWAfHfRYOC0fNvyG8ATpC/nLfLSRcVw0hWRHsAUSfOAZ4FBtudK6gmcImnD3M9M4BuF9sU9OJA2rwIMkbQL0A2YAezXyh1Ulb5+mtvcSbpz6S1JLaS7woouJV2BKGd5r+gO/Drfmv0O6SrNsDpjHw38EnhA0hvAa8DPypVsP5Nvvz6UtExU3oNzje0f5dd/lvQmsAppH9CgOuO3ie1plO6eAj4BPG376ULZLcBWSre2Hwl8LLf/r6TTSRuOv7S45hVCCMuLyCYeQicX2cRDCMsTNZhNPJaoQgghhNDlxBJVWICks0i3fBedbvvcpTT+ZcBmpeIjbV+7NMZvhKRtWHjp7U3bNZ/EHEIIYemKACcswPahHTz+4I4cvxHFJxyHEEJYNkWAE0In99jLs9n3kus7ehpd3uX7f7qjpxBCaIPYgxNCCCGELicCnBBCCCF0ORHghBBCCKHLiQAnNETSYEmWtGV+35zfH1uos66ktyWdKekoSZPzz7zC65qZviV9XtL9kqZJuk/S2flBgEhaWdJpkqZLelTSBEkbFdpulMsezXVOLzz1uL+kWZImSXpY0i2S9i603ULSzXl+D+aM3rXmWOzrQUk/r1L+kKRTCm0OkXRm4f3Bkqbm83xA0uG5fIykGYXP6va2/ZZCCCFUxCbj0KgW4DZSrqsRuexxYG/gp/n9AeSn99o+DjgOQNKc1hJeKiUMHQ7sZfvpnBvqi8D6wCvA8cDqwIdygtEvAeMlVW7NHg/81vag3HZ0Hv+IfPxW23vnsfoCl0uaa/tG4AxglO0J+fg2rXwWt9reW9JqwGRJfy2VrwpMknSZ7X+WznMv0hOr98hPK34/KfdURaPJUcNi8vqVY5n/6qxW6x18Ra2Hcqfs4iNHjlyc0wohLKIIcEKrJHUnPRtnAHAF7wU4c0nJNvvZnggMAS4GNmzHMEcBh1fSGNieB5yTx+9GSlewWS7H9rmSvgx8Krd/o/KsnhwADQdmVK6wFNmeLOkXpASfN5Jyaj1VOD6lkQnnLOn3AL1IWcwr5XNzeogPVGn243ye/81136DBHGBFkoaRU1usum7PtjYPBfNfnYVnvdxqvacbqBNCWHZEgBMasS8ph9Mjkl5SSu75Uj42Fhgq6VlgHvBf2hfgbA3cW+NYb+DJnFm9aGJuByl797tsz5b0ZG5bzb28d3VnFPD3vCR0HXCu7Vdam7CkdUi5o44F1iuU9wA2J+WZKutTnmtJMXfYNNsHVatkezTpKhVr9fpQ5FtZBCusvuaCae5r2LB7t5rHmpqaFt+EQgiLRQQ4oREtwGn59dj8/qz8/hrSF/xzwLjFMVjhScGrAz8BHgKqfYkrl6/QyvGqw1Re5KtB1wIDSQk3vy5pO9tv1mi7q6RJwHzgRNvTJPXP5fcDW+TyZ+ufaVWxRLWUdfvs0IbqnR/PwQmhU4lNxqGufJXiU8DZkp4gXfUYQg4QbL9FuiLxA1IG8faaBuyQ+5yS9+xcDaxKyjS+qaTVS212AB7IbRdIvCZpDWBjYHqN8bYH3s2ebvu/ts+xPYiU3bxPnbneant72zva/l2pfFtgG+Cbea9PtfPcsU7fIYQQFoMIcEJr9gfOt72p7WbbGwMzgI0KdU4l5Yt6cRHGOQE4pXhnFCm4wfZrwHnAr/IGYiQdDHQD/k7aR9Mtl5HrnAqMsf16eSBJ25I2Rp+V3w+UtFJ+3QSsAzzd3hOx/Ug+nyNrnOfIPA6SVql3Z1kIIYT2iSWq0JoW4MRS2aWkpSMAbE8j3z3VXravkrQecHUOUF4BpgKVJJs/Bk4BHpE0n7RsNdi2Id3GDvxG0k9JgftVxTny3rJSN9KG4O/mO6gA9gBOl/RGfn9EO5eXin4HHC5pgcSh+TzXB26QVFlCO6dQpbgHB+Cj+SpZCCGENlD+fgghdFJr9fqQ+590VusVwyKJXFQhLBsk3WO7X2v14gpOCJ1c7x5rxJdvCCGURIATlipJR5EeCFj0l/xgwGWGpD2Bk0rFM2wP7oj5hBBCaJsIcMJSVXzC8bLM9rW8t/8nhBBCJxMBTgid3PSX5zD40ts6ehqd2mX77dLRUwghLGZxm3gIIYQQupwIcEIIIYTQ5USAE0IIIYQuJwKcDiJpTv5vsyRL+k7h2JmSDsmvPybpX5ImS3pQ0ghJX8rvJ0t6S9KU/PrEQh8TJN1RGnOEpMPbMMfBeW5bFsoq8z22ULaupLfzvI8qzG1e4XXVp/XmOT2d60yVtE/p+H2SLiq8HyZpXOH9GpKmS9pM0hhJrxdTOkg6Pc933fy+OKfJkn6Uy2+WNLHQrl8u27NQd46kh/Pr82ucTzdJf86/k6mSbpPUPX9uU6uc++H5datzDyGE0LjYZLxseB44TNLvqzy19jzgc7bvy0/43cL2A8C5ADk/1ADbL1QaSFqLlKdpjqTNbM9o57xagNuAocCIQvnjwN6kdAeQbvueBgveJSVpTs4p1ZpRtk+R9GHgVkk9bc/P71cAPiFptZyy4Q/AFyXtbvsG4BfAObZnpAcD8xgpYeYFklYABrBg2oW5debUU9Jetq+uFBTvppJ0M3C47Yk12gMcBjxne5vcZgvg7QY+g0bmHhbBa1ecj1+tniT+4Amj67Ztampi5MiRS2JaIYQlJAKcZcNM4J/AF0lf4EU9gWcAbM8jJZdszX7AlaQM30NJ+Y/aRFJ34OOkL9krWDDAmQs8KKlf/rIfAlwMbNjWcYpsPyjpHWBdUtB3ICmr+IeBfYCLbFvSN4EL81Wu3VgweeVFeT4XAP1Jn+teDU7hZOBoUpLP9toA+Hflje2HAXLw1ZqG5y5pGDAMYNV111+E6S4//OorzJ9VPV3a07OW8mRCCEtcBDjLjhNJeZjOKZWPAh7OVw+uAc6z/Ua5cUkLcAwpwLmEdgQ4wL7ANbYfkfSSpB1s31s4PhYYKulZYB7wXxYxwJG0EzCfFPBB+rL/NLAF8G1SAIDt+yVdS0qyuW/pqtejwCBJPUifwwUsGCSsKmly4f0JtitLXncAgyUNAF5t52mcA1wnaf88v/NsP9pg29bm/i7bo4HRAD16bRn5Vhqg1dequSa/Qff3123b1NS0+CcUQliiIsBZRuQllrtIVy2K5b+Q9GdSQsgDSV98/Wv1o5TIsTdwW77a8Y6kPran1mpTQwtwWn49Nr8vBjjXAMeSgqhxLJrhkj5PCiqG5Hl/BJhp+9+SngLOkdTD9su5zVnAXrZvqtLfeNKVq52Ar5eO1VuiAvgl6SpOtUzgrbI9WdIHSb+v3YG7Jf0PsFBW80qT0vt6cw+LYLV9Dq557Px4Dk4IXU5sMl62HE/6Yl3g92J7uu3fkpZjtpO0Tp0+hgA9gBl5f04z6QuzYbn/TwFn5z6OAIaosM6Sr5rcA/yAlF18UYyy3df2rrZvzWUtwJZ5/OnAGqSlt4r5+aeasaTg63rbtepUZfvvwPuBj7WlXamPObbH2/4W6SrM/wIvkn4vRWsDL5TK2j33EEII74kAZxli+yHSHpu9K2WSPlMILDYnLQdV3ymZtAADbTfbbibtT2lTgAPsD5xve9Pcz8bADKD8z9xTgSNtV9/Y0E55g+0BwLaF8xhEOrdW2X4SOAr4TTuncBzww/Y0lPTxvMSEpJWBrYB/254DPCNpt3xsbWAgaRP34px7CCEEIsBZFh0HbFR4/wXSHpzJpA23B+XNxguR1AxsAtxZKct3UM3O+1sAjpb0VOWnxhxagMtKZZey8PLZNNvnNXRWbfMJ4GnbxTuIbgG2krRBIx3Y/r3t6VUOrVq6TfzEKm2v4r19QG3VC/iHpCnAJGAi713hOpj0+U8G/g4cU22OdeYeQgihQbJjf2IInVmPXlu6/8izO3oanVrkogqh85B0j+1+rdWLTcYhdHK9enSPL+gQQiiJAGc5ljcT31jl0G5LYF/NUaR9NUV/yQ8G7HQk7QmcVCqeYXtwR8wnhBDCgmKJKoROrl+/fp44sd7DlUMIoeuIJaoQlhPTX36d/S+9t/WKy4FL9tuho6cQQlhGxF1UIYQQQuhyIsAJIYQQQpcTAU4IIYQQupwIcNpA0rz8cLipkq6UtFYub5Y0t/QAuYPzse6SfitpuqRJku6R9LVCu6mF/neRdJekh/LPsMKxEZJel9SzUDanDfP9i6RuuXwjSRMkPZrndXp+6i6S+kualef6oKSf5/JDJJ1Z6v9mSf3y6yckrVtjHhMk3VF4f1Thc5pXeP3dfJ6H53qSdHSe5yOSbpK0daGfJyRdWni/v6QxdT6PQyTNzGM9JGl46fN9uvQ7XCt/Hn+t0d96kt6W9PVC2b9y2ycLY03Ov+snJK2bP7c9S319T9Jv6v0thRBCaFwEOG0zN+dM6gO8BBxaODY9H6v8nJ/LzwZeBja3vT3p8fxrlzuW1ARcCHzD9paktAhfl/SZQrUXSLmf2jPft4BvSBIpoePltjcHPgR0Jz1BueLWPNd+wOcl7diGMcvntRawA7CWpM0AbB9X+ZwKc+xr+4xS80OBnYHtbH+IlBX9CknF1M/9ikFPA8blcT8OHCVp48KxUaXfYb2UGJBue7+TQgoJ2zvl/n9WGSv/PFFodxELp88Ymsuh9t9SCCGEBsVdVO13B7BtvQqSegEfBQ6sJE60PZOFn58C6ct8jO17c70XJP0QGAH8Ldc5BzhE0km2X2rjfG/N8/0U8Ibtc/M48/KVjBmVqzUVtl+TdA8p/UB77QdcSco6PpQUpDTqSKC/7dfzfK6TdDtwEPDHXOcU4Ce5rGG2X5T0GLAB8J+2tC1oIQWcF0r6QCm1RD2XAL+UtIrtN5VSbGxIyku1aTvn0uW8esVo5r/6cusVCw6esEqb6jc1NTFy5Mg2tQkhdA4R4LSDpBVJmb3/WCjupZRjqOI7pOzR9zWYFXproJzXaWIur5hDCnIOA35OgyS9D9gLuCb3d0/xuO3Zkp4EepfarUPKqn0s8JFGxytpAY4hBTiX0GCAI2kNYLUqOZnKn8nFwLck9aYNJG1Cyhp+f6F4uKTP59cv2x5Qp/3GQJPtuyRdTMri/qtGxs7B1V2kq3kTSIHfONtOF9gW/lsqZFmvjD8MGAaw6rpNjQzb6cx/9WXmzyonW6/v6VlLaDIhhE4nApy2WTV/8TSTgoTrC8em56WJd0nap/S+8jTfnrY3LPUtoNpTF8tlZwCTJZ3ahvlCuoLzR+CbNcYpjr+rpEnAfOBE29Mqe20amN97HUrrk4Km2/KX9zuS+tieWqtNA8qf0zzgZODHwNUNtB8iaQCwBfA1228Ujo2yfUqD8xhKCq4AxpI+24YCnKyyTFUJcL5cOLbQ31KZ7dHAaIAevbbqkk/rXGH1Hm1us0H3tl/BCSF0TRHgtM1c230lrQn8lbSsVN43UvQAsJ2kFWzPz2kJjlP1zcHTSHteriiU7Zj7eJftVyRdCHyr0fkWCyRNIy0bFcvWADYGpgPrkPbg7F3q60XSFamitUn7gmoZktvMyFcm1iB9mR/d2sTzVaXXJH3Q9uOFQzsA/yhV/xMpwJnWWr+kKyXflvQ/wN8kXW372QbalbUA60uqLI1tKGlz24822P5y4FeSdgBWrSxNhvesvs+w1iuVnB8P+gshZLHJuB1szwK+CxwuaaU69R4jLan8Mi9rkTfIqkr1s0j7a/rmeuuQ9upU2yDwK+DrtC9AvRHopvfu8loROJW0/+f1Ou3uBj6eN0OTr+isQv39Ky3AQNvNtptJAVt5c209JwNnSFo1j7k7afP1hcVKtt8GRgHfa7Rj23eQAqPD2jAf8jy2IC2ffaBwbifQhnOzPQe4mbTkeFH92iGEENoqApx2sj0JuI/3vtR6lW7t/W4u/yrpqshjecPuDaTNs+X+ngE+D/xB0kPA7cA5tq+sUvcF4DJSgNHWeRsYDBwg6VHgEeAN0kbdeu2eIwUDV+Vlr9OAltL+ovslPZV/xgObkO4yqvQxA5gtaacGp/trUmA1RdLDwE+BQbbnVqn7R9oe8J0EfEnS6vn98NLvsDmX71Y4r6dIV+0uK/V1KYW7qRp0EbAdaYmrqNbfUgghhAZFss0QOrkevbbybiMv6OhpLBMiF1UIXZ8i2WYIy4dePbrFF3sIIZREgNPJ5b06N1Y5tJvtF5f2fJYFkr7Ewntr/mn70Gr1QwghdD0R4HRyOYipe0vx8iY/xPDcjp5HCCGEjhMBTgid3OOvvMmQ8Y919DSWqnH/16bnOoYQlkNxF1UIIYQQupwIcEIIIYTQ5USAE0IIIYQuJwKcEEIIIXQ5EeAAkpokjZU0XdIDkq6S9CFJCyWFVHK0pEclPSLpJklbF45/WdIUSfdLmippUC4fI2lG4em0t+fyQyTNlDQp93mtpJ0bmPP7JL0g6YRS+c2SnlRO/pTLLpc0R9I2hfFfKsznhjrjfCh/Ho9JelDSxZLWl9Rf0qw874cknVJoUzmn4tN4t5LULGlubvOgpLskfbHU7kxJRxXazWvtib6StsjnPTn3O7rYX5XPp19+/YSkcpbuydV+74Xj/SVZ0lcKZdvnssPz+6q/60L9CZLuKJWNkPS6pJ6Fsmo5y0IIITRgub+LKgcClwHn2R6ay/oC69dociiwM7Cd7dcl7QFckYOcdYGjgB1sz5LUHViv0PYI25dU6XOc7W/nsQcA4yUNsP1gnanvATwMfE7ST7zgI6lfAT4O3CZpLWADANtTyLeUSxoD/LXGfMh13g/8Dfh+JWVEnl/lnG61vbdSrqhJki6z/c/yORX6ayZlyt4+v/9gPtcV8q3d5HkeBxyX68xpLbM2KXXCKNsTcpttWqlftLqkjW3/R9KHG2wzhZRI9I/5/VBS2o6iqr/r/PvYAZgjabOcvqLiBeAHVEnlsbyYdcVpzJvd+uObDr68Zgq4BTQ1NTFyZLV0biGErm65D3CAAcDbtn9XKbBdzENUdiTQv5KY0vZ1+V/oBwGTgFeBOfnYnMrrRtm+KV+BGAYMr1O1BTgd+CbwMaB4RWAs6Uv3NuD/gPHA1uUOGnAgcEcxH5btmyBdySiUzVXKT/WBtnRu+3FJ3ycl+1yU59ZsADxV6HdKG9peTApWTiF9phcBX2ilzZPAGpLWB54HBgJXNTjefsCVwHOk31HxCtw5pISrJ9l+qV4nkoaR/kbotu6GDQ697Js3+0Xmz3q+1XpPz1oKkwkhdGoR4EAf4J5GKkpag5RFenrp0ERSADGG9MU1Q9KNwPhSssyTJR2dX0+zfVCNoe4lZQuvNY9Vgd1ynbVIX8zFAOdGUtLOFUlfosNIiSrbqqHPRlIPYHPglkLxEEm7FN7/T43m9wJbtmNuRaOAv+dA8zrgXNuvNNj2EtLv7RTgs6RAtbUAp9LuAFJQey/wZul4rd91C3AM6e/kEhYMcOaQgpzDgJ/XG9z2aGA0wNq9t+kyCeVWXGOdhupt0L3xKzghhOVTBDiLh0iJuudJGgh8hBSAjJK0o+0RuV6tJapq/dWzN3BTXiK7FPippOG25+Xj80hXb4YAq9p+orAlZ3HaVdL9wBbAibafLRyrtkRVrY9FnpjtcyVdS7qSMgj4uqTtgFpf/MXyl4CXJQ0FHgReb3DYi4FxpODsItKyZdFCv+t8xac3cJttS3pHUh/bxT0/ZwCTJZ3a4Dy6lDX3+V5D9c6PB/2FEFoRm4xhGrBjIxVtzwZey3tHinYAHsh1bPsu2yeQrp7s1445bU/6sq2lBdhd0hOkKyzrkJbaisYCvyZ9EbdXa5/Nrba3BbYBvpn3LrVVa+faENv/tX2O7UHAO6SrTy8CPUpV1ybtdSkaB5xFClQaHe9Z4G3g01TPBVbNkDyfGfl310z6Gyn2+wpwIfCtRucSQghhYRHgwN+BVSR9rVIg6SPApjXqnwyckZeJkLQ7sAtwoaQNJRXTOvcF/t2WyUj6JGlJ6Q81jq+Rx9vEdrPtZtLG55ZS1VtJyx8Nf2lXcSGws6TPFMYfWN7Ea/uRPFabNsfmfU6nkAKxdstzWim/biIFfE8DdwMfz2Xku6dWAf5T6uIyYCRwbRuH/hlwZOHKWWtagIGF39uOlAKc7Fek5ce4whpCCO203P8PNC8VDAZOk/Qj4A3gCeB7wBaSnipUH076Mu4BTJE0D3gWGJQ32vYETpG0Ye5nJvCNQvvivgyAj+b/VvardANmAPvVuYPq/4C/2y7u+ZgAjJS0SvG8SMFDu+Vz2pv02ZxGumJxP2mPSHmzxO+AwyVtVjqnim8B/wV6SZoEvJ+0IfvXxTuo2mkP4HRJb+T3R1SWyyQdBlwlaQXSHpcW2/NL5/kqcFKu3/Cgtm+vc7j8u/4csAlwZ6H9DEmzJe1U6vcFSZdRf5N5CCGEOrTg3cUhhM6mX79+njhxYkdPI4QQlgpJ99ju11q9WKIKIYQQQpez3C9RLcsknUV6YF/R6YthSac8zjbAn0rFb9reqVr9jiLpKNKt2UV/yQ8GXNxj7UletiqYYXvw4h4rhBDC4hdLVCF0crFEFUJYnjS6RBVXcELo5J5+5W1+ctnTHT2NDnf84DY9SDuE0MXFHpwQQgghdDkR4IQQQgihy4kAJ4QQQghdTgQ4YZFJmpP/2yzJkr5TOHampEPy649J+pekyZIelDRC0pfy+8mS3pI0Jb8+sdDHBEl3lMYcIenwBuc3L/c5TdJ9kr6fH/yHpP6SZuXj90u6IT+wEUmHSJopaZKkRyVdK6mcc6o81hhJr0tavVB2ev5c1i2UDc5lWxbK+kmaKmnl/L6XpMfz06tDCCG0QWwyDovb88Bhkn5v+63SsfOAz9m+L2c638L2A8C5ADk/0wDb7+aKkrQWKdfXHEmb2Z7RjjnNtd0399eTlIJiTd7L2H2r7b3z8RNIqS8qx95NGippADBe0oA6T5oGeIyU9POCHEgNIKWOKGohJUQdCowAsD1R0i3A4cDxpPxYR+UcaMulByeM5K3ZMxuqe/Bljf3vrKmpiZEjRy7KtEIInUAEOGFxmwn8E/giC+fT6gk8A5DzNz3QQH/7AVcCz5GCgRMWZXK2n5c0DLhb0ojiMaU8DauTApRqbW+SNJqUK6xeGoWLSIk1LwD6kz6PvQrjdCc932gAcAU5wMl+Atwr6R1gJdtVc4nlcxgGsMZ6Xffuobdmz+SNWc81VPfpWUt4MiGETiUCnLAknAhcLemcUvko4GFJNwPXAOfZfqPcuKQFOIYU4FzCIgY4ALYfz1dWeuaiXSVNJuXXeo0UZNRyLykRZj2PAoMk9SDN/wIKAQ6wL3CN7UckvSRpB9v35rm9Iukk4DfAVnXOYTQwGmCD3tt12YdZrbzGeg3XXad741dwQghdXwQ4YbHLSSTvAg4slf9C0p9JyTEPJH3596/Vj6T1gd7AbTkp6juS+tieuhimWcyqWVyiOpKUWfwbVVst2K6e8aQrTjuxcEDUApyWX4/N7+8tHN+LFNBtBTzc4Hhd0ocH/bDhuvEcnBBCUWwyDkvK8cCRlP7GbE+3/VtgN2A7SeWs5EVDSJnbZ+T9Oc2koGGRSPogMI+0X6jsCuATdZpvD9Tbf1MxFjgWuL6YvTyf76eAs/M5HUHKvK58fG/S/qA9SRnJuzUwVgghhJIIcMISYfsh0h6bvStlkj5T+SIHNicFGa/U6aYFGGi72XYzsCOLGOBIWg/4HXCmq+cp2QWYXqPtJ0n7Xsp7ixZi+0ngKNJSU9H+wPm2N83ntTEwA9hF0qrAqcChtqcAE3IfIYQQ2iiWqMKSdBwwqfD+C8AoSa8D7wAH5c3GC5HUDGwC3Fkpy0tfsyVVkoAeLel7heMb1ZjHqnmPzUp53D8Bvyocr+zBETAL+Grh2BBJuwDdSIHIfq3cQfUu27+vUtxC2qNUdClpyW4v4PJ8ZxmkzceTJY2x/WgjY4YQQkgi2WYIndwGvbfzl06+qqOn0eFiD04Iy4dIthnCcuIDa60UX+4hhFASAU7oEvLm3RurHNrN9otLYLyzSM+yKTrd9rmLe6wQQghtFwFO6BJyENN3KY536NIaK4QQQttFgBNCJ/f8K29z1mWNPe13eXLo4PU7egohhA4Ut4mHEEIIocuJACeEEEIIXU4EOCGEEELociLAaSdJgyVZ0paFsg9JukrSY5IelHSxpCGSJuefOZIezq/Pl9Rf0l8lNUt6KieALI4xWdJHJY2Q9HShn8mS1mplfqfnNisUyg7Jc96tynnsL+my3PdjkmYVxtq5xhgrSTpR0qOSpkq6S9Je+dgTkqZIul/SPyRtWmg3r3QuP8rlN+fP535JD0k6s3ie+fPbptDuJUkz8usbasxxBUln5PlNkXS3pM0q/ZXqHiLpzPx6RP5ceheOD89lNZ+/IOnLhfOeKmlQ4dz6Feo1S5qaX/fP/X6lcHz7XHZ4rbFCCCHUFgFO+7UAt5FTB0h6P/A34Le2e9v+MPBbYJrtvrb7AhNJT+/ta/vgSke2nwD+A+xaKcuB0+q278pFoyr95J+aKQ5yUDM491nOqzQlz71iKHBfnsfgPM+vkhJQVsa6vcZQxwIbAH1s9wE+C6xeOD7A9rbAzcDRhfK5pe549sEAACAASURBVHMpPtn3oNxmW+BNUrqCd9meUvg8rwCOyO93rzHHIcCGwLa2t8mfS730EEVTWDA1xP6k9BNVSdqIlFphl3wOHwPub8NYQwrv3/29hBBCaLu4i6odJHUnPQNlAOlLdgTpUft32L6yUs/2TW3o9iLSl9o/8vuhuaw9BgBTgXGkYObmwrFbSakJVgJWIWXrntzWAZSSQH4N2Mz2mwC2nwMurlL9DuC7benf9luSfgg8Jmk72+39st8AeKaS8NL2U21oezkwCPilUoLOWcDbder3BF4F5uSx5lReN+BJYA2lDOrPAwOBeDxxDbdNOIHXZ8+sW+dfl61Y93hTUxMjR45cnNMKISxDIsBpn32Ba2w/kpdJdgD6APcsQp8XA5Mkfcf2O6R/zR9QOD5c0ufz65dtD6jTVwspOJoAHC9pJduVL2YDN5CyVa9JCtA2a8d8ewNP2p7dQN2BpGChopIbquIE2+PKjWzPk3QfsCXtv5pxMXCbpF1JDwK8wPakVtpUzAb+I6kPKdAZB3ypTv37gOdI2c9vBMYXA94GXEL6nU8C7iVdwapK0jBS4k96rFcrBVfX9frsmcyZ9WzdOnNmLaXJhBCWSRHgtE8LcFp+PZYFl3zaxfazkqYBu0l6Dnjb9tRClVG2T2mtH0krA/8LDLf9qqR/AXuQls8qxpKuqKwJ/AD4yaLOv4abClckFlqiarAPtV6lNttPSdoC+FT+uVHSAbarPfUYUgBYNJZ0NW1PYDfqBDg5IBsIfCTXHSVpR9sjqvRbbayLSUHUlqQAterepzzWaGA0wCa9t1vuEsp1W2O9Vuus2b31KzghhK4rApw2UkoJ8CmgjyQDK5K+qI4BPrmI3VeWqZ6j/ctTA0mByxRJkLJgv04hwLF9V74qMTdfhWrPOI8Bm0ha3farNeoMAF4DxgC/AL7flgEkrQhsAzSUvbuWvIR2NXB1Dh73JV3NmStpZdtv5aprAy+Uml8JnAxMtD27tc/KKXvtXcBdkq4HziUtYb4I9ChUXWisHOS+DXwaOIw6Ac7ybpdBP261TjzoL4TlW2wybrv9gfNtb2q72fbGwAzgEWBnSZ+pVJQ0UNI2bej7UtLVlyGkKwft0QJ8Nc+tmbT8tEfeM1P0Yxbhyo3t14E/Amfkq0ZI2qCwjFapNxf4HnCwpLUb7T/vEToB+I/tRjfqVutnB0kb5tcrkDYv/zsf/gfw+XxsVeBzwAL7pvL8jwSOa2CsDfNyZUXfwlg3A5/XexHSF8tjZT8DjrQ9r9WTCyGEUFMEOG3XAlxWKruUtMl4b+A7+bbpB4BDSMszDcl3Rt0JPGd7Runw8NKt1c3l9jmI2ZMFr9a8Rrrb67Olsa5u4yboao4GZgIP5FueL8/vF2D7GdIVqUr+plVL51K8i+rPku4nbZJejbT3ZVH0BK7M87sfeAc4Mx87DPi/vB/oTuAvtm+pMv+xtu9tYKyVgFPyLe6TSYHqYfnYaNIG5PvyvqLuwEJLjrZvt315uTyEEELbKF1RDyF0Vpv03s5HnnxdR09jmRNLVCF0TZLusV3zeWQVsQcnhE6u51orxZd5CCGURIDTSUnaEzipVDzD9uAlMNZlLHwr+ZG2r13cY7VX3uv0p1Lxm7Z3WkLj/Yv0HKGiL9iesiTGCyGE0DYR4HRSObhYKgHGkgiaFrccWDR66/niGG+JBE4hhBAWjwhwQujkXn75HcZdWr67PVQM2W/djp5CCKEDxF1UIYQQQuhyIsAJIYQQQpcTAU4IIYQQupwIcEIIIYTQ5USAEzqMpI0kTchPfp4u6XRJK0uaJKlvrvM+Sa8VU0BIuienYDhE0nxJ2xaOTa32lOfC8e6Sfp/HmybpFkk7FY4PlmRJWxbKmiXNzU9dfkDS+TmVRGvnd7qkp3OKiGL5QEl3VZ54LGmcpE3ysTGSZhSe8nx7Y59mCCGEoriLKnSInJNpPPBb24NyYs3RpJxPt5MSTU4GtgMezu8vkLQa8EHgPlJeqaeAo0hpERpxNil32Oa250v6IPDhwvEWUmqLoaQkmRXTbffN87yelLfqz3XObwVgMPAf4BOkXFTkJKe/Bvax/WAu2wdoBp7MzY+wfUmD5xOAv11xHHNeXShLSDo2ofa/45qamhg5cuSSmlYIoQNFgBM6yqeAN2yfC2B7nqThpODjO6Sko78hBTa/I+X1AvgocG+uD/BX4BOStrD9cL0BJfUCdgIOsj0/j/s48Hg+3h34OCkL+hUsGOBQmOddwAdaOb8BpHxa40hB0825/Ejg+Epwk/u8opW+qp3LMGAYwLrrbtTW5l3OnFdnMmvWM1WPzZq1lCcTQlgmRIATOsrWwD3FAtuzJT1JCgx+mYt3Bo4BWiStnt//s9BsPjCSlBn9iw2MOblOpu59gWtsPyLpJUk7lJNsSno/KUg6rGoP72khJRidABwvaSXbb+c5LJRks+RkSUfn19NsH1SuYHs06YoXvXr1Xe4TynVffb3ax7rXv4ITQuiaIsAJHUVAtS/mSvnKkpqALUlLVHeTAoudSUs8RRcCR0kqp5NoqxbgtPx6bH5fCXB65QzhmwOX2L6/VieSViZdgRpu+9Wc1mEPClnec711gBuBbsBo25XAJ5ao2ugz+xxV81g86C+E5VMEOKGjTAP2KxZIWgPYGJgO3AHsDzxj25LuJC0ffRS4s9jO9juSTiUt/7Q25naSVqgsURXGXoe0bNZHkoEVAUv6Ya5S2YOzAXCzpH3qLC0NBNYEpuRltG7A66QAZxqwA3Cf7ReBvpIOB7q3MvcQQghtEHdRhY5yI9BN0sEAefPuqcAY26+TlqGGkwId8n8PBp61/UqV/sYAuwM11ypsTwcmAsfkTc5I2lzSIFIwdb7tTW03296YtB9ol1IfzwA/An5c59xagK/mfppJiUr3kNSNtJx2lKTixuZudfoKIYTQDhHghA5h26S7jA6Q9CjwCPAGaS8NpADng+QAJwcWK5LusKrW31vAGUDPVob+KtAEPCZpCvAH4L+koOSyUt1LgQOr9HE5KTjbtXwgBzF7UliOsv0a6c6sz+akoIcB5+fbxP9JuovrwkI3JxduE5+cl7xCCCG0gdL3TAihs+rXr58nTpzY0dMIIYSlQtI9tvu1Vi+u4IQQQgihy4lNxqFLyncurVIq/kJeIlpcY+wJnFQqnmF78OIaI4QQQvtEgBO6JNs7tV5rkce4Frh2SY8TQgih7SLACaGTm/XyO1w97oWOnsYyb68h8TycEJYnsQcnhBBCCF1OBDghhBBC6HIiwAkhhBBClxMBTicgaV5+4Nt9ku6VtHMu7y/pr6W6YyTtn1/fLOnh3O5uSX0L9daUdL6k6fnnfElr1plDs6S5kiZJelDSXZIWSm4paYKkOwrv95B0R+HJwSvmc9lZ0hZ5jpNzn6Nb+Rw+KumWfE4PSTpbUjdJh0iamft5KGclr7QZIenp0oPz1sqf3ax8Pg/nfvcutTtc0lm5zQP5/Ct97F9jjmMkvZ4Tg1bKTpdkSesWygbnsi0LZf0kTa082E9SL0mP5xQWIYQQ2iA2GXcOc233hXdvTT4B+GSDbQ+yPVHSl4CTgU/n8j8CU21XUiUcA5wNHFCnr+m2t8/1PwiMz3mdzs1la5HyLM2RtJntGbavk/Rl4Cu5/+8Ad9u+XdK1wCjbE3L7bWoNLGl94C/AUNuVgGk/oBJIjLP9baWcUg9LusT2f/KxUYVElpX+AG61vXd+3xe4XNJc2zdW6tk+NB9vBv5a+T204jFgEHCBpBWAAcDTpTotpKcbDwVG5LEmSroFOBw4HjgLOMr27AbGDMBf/nYcs+fMrHrsor9V//dcU1MTI0eOXJLTCiF0gAhwOp81gJfb0e4O4AgASb2BHYEhheO/IKUv6JVzNtVl+3FJ3yfljzo3F+8HXAk8R/riPiGXDwduy1d2vk1KmAmwAfBUoc96z6g5FDjPdiV1g4FL8vkU5/WipMdy3/+p0k+t85ks6Rd5fje2Vr8VF5E+2wuA/qS0E3tVDkrqTkocOgC4ghzgZD8B7pX0DrCS7YuqDSBpGDAMoOe6Gy3idLuO2XNm8vKsZ6oee3nWUp5MCKFDRYDTOawqaTLwftIX96fa0cdAUg4lgK2AybbnVQ7anpfH2JqUzbsR9wJbFt63AMeQApxLyAGO7WcknUYKsr5r+6VcfxTwd0m3A9cB59ZIpAnQBzivtQlJ2oT0Od1fKB4u6fP59cu2B9Q5nyNaG6MBjwKDJPUgfSYXUAhwgH2Ba2w/IuklSTvYvhfA9iuSTgJ+Q/o9VWV7NDAaYPNefSPfSrZG95q5VunWvfYVnBBC1xMBTudQXKL6H1Kixj5ArS+2YvmfJa1GSlS5Qy5Tjba1ymt599JJXkLqDdxm25LekdTH9tRc5SzgRNtj3p2kfW5ephpIWtL5uqTtbL/ZhjlUDJE0ANgC+JrtNwrHFlqiau18FoPxpKtYOwFfLx1rAU7Lr8fm9/cWju9FChK3Ah5ejHPq8g74zFE1j8VzcEJYvsQm404mL9GsC6wHvAj0KFVZGyg+9e0gYDNStuqzctk0YPu8PwSA/Ho74ME2TGf7Qv0heS4zJD0BNJO+4Cvznk+V4Mn2f22fY3sQ8A7pSk0100jLarWMs701sCtwqqT2/LO8eD6LaixwLHB9PncA8h6hTwFn58/pCFJwVtmEvTewJikj+clK2clDCCG0UQQ4nUy+62ZFUnDzKLChpA/nY5uSgpTJxTa23waOBj4m6cO2HwMm5bKKo4F787FG5tEMnAL8Ohe1AANtN9tuJgUjQ6s2fq+PgZJWyq+bgHVYeDNuxZnAFyXtVGj/+XIgkwPAPwGHNXIehb62BX7Ke0HgIrH9JHAUaampaH/gfNub5s9qY2AGsIukVUl7mg7N+5Em5D5CCCG0USxRdQ6VPTiQllG+mPfPzMt7S86V9H7gbeCrthfaTml7rqRTSXfofCX//DpvyBVpf8xXWplHL0mTSHtcXgV+nZeZmoFNgDsL482QNFvSTrb/VaO/PYDTJVWWk46w/Wy1irafkzQUOEVST2A+cAtpKajsJNJG3ePz++IeHEh7YAB2zefTDXietD9oUTcYF+f8+yrFLcCJpbJLgQNJS1OX234gl48AJksaY/vRxTWvEEJYHijdjBJC6Kw279XXZxx/Q0dPY5kXe3BC6Bok3WO7X2v14gpOCJ3cmj3eF1/eIYRQEgFOWEB+2N6fSsVv2t6pWv0lMP6epCWmohm2By+N8Rsl6SzSs2yKTq889DCEEELHigAnLCBvbm3kab1LavxrgWs7avxGVZ5wHEIIYdkUAU4IndycF9/h1j9VT0+wvNj1C7Uf8BdCWD7FbeIhhBBC6HIiwAkhhBBClxMBTgghhBC6nAhwQkMkzcn/bZZkSd8pHDtT0iH59cck/UvSZEkPShoh6Uv5/WRJb0makl+fWOhjQs42XhxzhKTDG5zfvNznNEn3Sfp+JRWFpP6SZuXj90u6IT8sEEmHSJopaZKkRyVdK2nnVsYaI+l1SasXyk7Pn8u6pflUfn5UqLuepLclfb3U7xOSLi2831/SmEbOP4QQwoJik3Foj+eBwyT93vZbpWPnAZ+zfZ+kFYEt8pN5z4X0JQ4MsP1uvixJa5ESgc6RtJntGe2YUzEhaU9S7q01gZ/n47fa3jsfPwE4tHBsnO1v52MDgPGSBtiul5fqMVKC0AtyIDWABdNMvDufKg4gPfW5BSg/7bifpK1tT2v1jLu48687jlfmNLZ5+g/Xr9hqnaamJkaOHLmo0wohdBIR4IT2mAn8E/gi8IfSsZ7AMwA5ncQDtG4/4EpSBu2hwAmLMjnbz0saBtwtaUTxWE5quTopQKnW9iZJo4FhwPA6w1xESjB6AdCf9Hns1eAUW4AfABdK+oDtYmB0CvATUpLUmvL5DQNYf52NGhy2c3llzkxeml01c8fCZi/ZuYQQOp8IcEJ7nQhcLemcUvko4GFJNwPXAOfZfqPcuKQFOIYU4FzCIgY4ALYfz1dWeuaiXXM+r3WA10hBRC33Al+vcxxSotNBknqQ5n8BCwY4xfxhACfYHidpY6DJ9l2SLiYFSb8q1LsY+Jak3q2c32hgNMCWm/XtkvlW1ure+K3fq67e2BWcEMLyIwKc0C45meZdpCSRxfJfSPozKZHmgaQv//61+pG0PtAbuM22Jb0jqY/tqYthmiq8Li5RHQmMBL7RQLt6xpOuOO3EwgFRrSWqoaQgBmAs8EcWDHDmAScDPwaubnAeXdLBezSeSD2egxNCKItNxmFRHA8cSenvyPZ0278FdgO2k7ROnT6GAD2AGXl/TjMpCFgkkj5IChaer3L4CuATdZpvD9Tbf1MxFjgWuN72/Aan1gIcks/1CtLns3mpzp/y/DZpsM8QQgglEeCEdrP9EGmPzd6VMkmfyftcADYnBRmv1OmmBRhou9l2M7AjixjgSFoP+B1wpu1qyze7ANNrtP0kaW9LeW/RQmw/CRwF/KbBeW0BrGb7A4XzPYHS+dp+m7TU971G+g0hhLCwWKIKi+o4YFLh/ReAUZJeB94BDsqbjRciqZl0leLOSlle+potqZLc82hJ3yscr7WjtrLnZaU87p9YcOmnsgdHwCzgq4VjQyTtAnQDZgD7tXIH1btsl++CKs+n4hrgDeCyUr1Lee9KUNEfgaMbmUMIIYSFqfo/cEMIncWWm/X1H35xfUdPo0PFHpwQlh+S7rHdr7V6cQUnhE6u+zrviy/4EEIoiQAndBp5s/KNVQ7tZvvFJTDeWcDHS8Wn2z53cY8VQghh8YoAJ3QaOYip9XTgJTHeoUtrrBBCCItXBDghdHKvv/AOk86udjd8ANj+qz1brxRC6HLiNvEQQgghdDkR4IQQQgihy4kAJ4QQQghdTgQ4S5CkeZImS5oq6S+SuuXyjSRNkPSopOmSTpe0cj7WTdKfJU3J7W6T1L3OGJb0p8L790maKemvhbJ9Jd0v6aHc776FY2MkPS1plfx+XUlPSNomz32ypJckzcivb5DULGlqaR4jJB2eX39M0r9y/QfLGb1L7Q7J850saZqkSyqfU6HOfZIuKpWNkbR/jT6HS3pD0pqFsv75s/psoeyvkvrn1ytJOjH/TqZKukvSXvnYE/lzq3weZ9Q5n6rnns/zzPz62kJfkyX9V9K/Cuc1o3Ds9lpjhRBCqC0CnCVrru2+tvsAbwHfyGkMxgOX294c+BDQnfREYIDDgOdsb5PbfQV4u84YrwF9JK2a338aeLpyUNJ2wCnAINtbAvsAp0jattDHPODLxU5tT8lz70vKmXREfr97A+d9HjAst+3De8klaxmX+96a9DkNKcz/w6S/009IWq2BsSGlf7gbGFwqf4qUWqGaY4ENgD75c/8ssHrh+IDK52H7u3XGbvXcbe9Z+Gw/DsxmwacWH1EYa+c6Y4UQQqgh7qJaem4FtgU+BbxReZaK7XmShpOSTf6c9CX770oj2w830PfVwGeAS0hf7hcBu+ZjhwPH256R+5sh6QTgCFJaBYDTgOGSWs2/1KCewDN5vHmkfFWtkvQ+YDXg5ULxgaS0Cx8mBWcXVWla7KMXKWA8AvgJMKZw+D5gJUmftn19oU034GvAZrbfzPN+jtYDs2raeu6nA1cV5xPaZvRNx/PSazNrHl/llhVrHmtqamLkyJFLYlohhA4WAc5SkL+49yLlI9oauKd43PZsSU8CvYFzgOvy8suNwHm2H21liLHAz/Ky1La5j0qAszXpCk7RRKD4jJcngdtIAc+VDZ5Wr1KupabCOKOAhyXdTDrn82y/UaevSi6oDYBHSnMYQroqtQXwbVoJcHgvwLsV2EJST9vFe6h/mX+KAUVv4Enbs+v0e5OkSk6t82yPqlGv4XOXNBjoB+xUOnSypMoVnWm2D6rSdhgpKShNa9dKz7V8eOm1mbzw6rO1K7y69OYSQlh2RICzZBUTLt5KSqD4TaBaAjABtj1Z0geBPYDdgbsl/U+95I+271dKXNkCXFWt3wbKjictRf2ttZPKpuclltRhYZ+N7V9I+nM+hwPzvPrX6Wuc7W/n5buzSFdfTpT0EWCm7X9Lego4R1IP2y/X6WsoMNj2fEnjgQNyn5W53SoJSbvW7KG6AbZfaK1So+cu6QPAGcCelatGBUfYvqSVcUYDowG2au67XCeUW3u1+mkqVlmj/hWcEELXFAHOkjW3GAQASJoG7FcqWwPYGJgOYHsOaZ/OeEnzgf8FWstufQXpCkp/YJ1C+TTSVYL7C2U7UFo6sf1YDsY+18iJtcb2dOC3edlrpqR1WkunYNuSrgS+A5xICg62lPRErrIG6bM7u1r7vK9oc+D6FCuxMvD/7Z15uF1FlbffHwGBMEYhBJmCgASMJGAURUAis9JCGjRcQcGPFttmkm4mAe2AIhCwASXaIkJAMQTDpChTM8gUgRAyQgKJQQaBhHlImMLv+6PqJDsnZ7oDuTnnrvd5zpOzV61dtar2gVq3au1af6fg4GTOIMXivJevZwEbS1rDdqf/3q/U9zI7RYrVOct2Q9t3QXUOH3pyzfI46C8IeiYRZLzsuQ3oLembAJJ6AT8FRtueL+nzkvrksg8BW1OIyanBJcDptqeWyc8Fvp9XeMj/npzbLOcMUsxOp5D05TyJQ3I4FgKvNHj7jsBsSSuQVl+2sd3fdn9gX5LTU402YERJ3/ZHgQ0kbVJUsn0L0AcYlK/nk1bXfqbFb7OtL+ngBm1eRIN9P44Uh1XueAVBEARdRKzgLGPyKsUw4BeSfkByMv9CcjoANiP99a9c9mfg6gbqfZoUsFounyTpROBPklYivZF1gu1JFXSnS5pIWuHpDN8AzpM0n7RKclAOuK1GKQZnBdKbTocCOwPP2H6moHcXsLWk9fP1rySdn78/RYoD2rus7mtJ21b3l8nPAK4vXJ9Kis15RNJbpLfTflgoL8bgTLH9zSp9qdj3xT4P5HaeLothetn20Py9GIMD8Bnb71RpLwiCIKiA7B69fR8ETc/W/Qf7ilNv6W4zlltiiyoIWgtJD9keUk8vVnCCoMnpvc6KMYkHQRCUEQ5OE5CDVG+rULRrvcDd5QVJ3yIdYljkXttHVNJf3pE0inRIX5ELSucbBUEQBN1LODhNQHZiBtdVXI7JE3/LTP7N6pgFQRD0FMLBCYIm5+257/L4hc93txndzhZHrtfdJgRBsBwRr4kHQRAEQdByhIMTBEEQBEHLEQ5OEARBEAQtRzg4QRAEQRC0HOHgVEDSMEmWNCBf95e0QNIkSY9I+l9JK7RDfnk+RbhU/46SHpA0I38Oz/JdJI0vs2VFSc/n1AGjJc3J9U6SdF/WOVTSPEkPS3pc0s2SdqjTx1JdkyU9lm3coExn2zwOe+ZrSbpH0t4Fna9Juil/P0XSdElTsn3lWbKLdd8paWbWnSHpQklrF8oXFvo5SdJJWb6SpLNyP6flcdw7lz0haZ1atuR2h+Tva+V+z86fyyWtVXjmlnRUwaYLJR1aZ0znS1qjILsg17NOQbbE7yvLhuT+lFJFbCbp70p5yoIgCIJ2Em9RVaYNuId0xP+ILJtte7CkFYHbgf2AiQ3IewG3kpJYXiGpH/B7YD/bE/PEd7OkZ4AbgQ0l9bf9RG53N2Ca7WeVjvuvlml6rO0jASQNJSXqHForC3mpLqWKv0dKRzCwkBagNA5twM05zcS/A3+QdAfQi5TyYC9JnwP2Abaz/Xbu14fqjPNBtifkSf1MUuqEL+SypRKVZn4ErA8MzO2sV7iH3P9GbfkNaWxLecFOIyXy/GounwscI+lX7UiVMIuUM+t3Svm0hgLPlOks9fvK43AXKU/VT0gJQk+x/VqD7bYcF9x7Ji++Oa9h/ZUeqJ41vES/fv0YOXJkZ8wKgqBJCAenDEmrkw5wG0rK0D2iWG77vbxysjnJkaknXyjpAaC0OnIEKbHmxFz+gqQTSEki/yzpD8Bw4OysfyAwpj19sH2HpIuAw4FjG9A3KX/SMFIup+uz03MAsDtwt6RVbL9le5pSxu8TgdWAy23PljQIeMH226V+tcPed/IYzJI0yPbkSnqSegPfBjYttPM8cFWZ6vr1bJG0OfAp0liXOD3bsBkpSeY84F7gEODXDXZnTK7zd6TM7vdSyI9V5/d1MjBR0nvASrarPnelVb/DAT7aZ8MGTWsuXnxzHnPffK7xG9784GwJgqD5CAdnafYDbrL9mKSXJG0HvFQqzJPsriyZiLGWfBVgexaf4vsJ4LKyNidkOaQJ8iLgbEkrA19iSSelmIhxuu2DqvRjIvCdOn2tdM8A0krK54E52Xm5M9txTdY7Leu+A5TygdwC/FDSY8D/kVaU/tpow9kRnJzbnwysqiWTUZ4JPAo82cCqRiO2bA1MKiYBzTZMIj2LKVl8FnCjpEsa7MrjwL5KGeHbSI5OMQHoUr+vgrP7iqSzgV9k+6pi+yLS74RPbjyoJRPKfWS1ddulv9Laja3gBEHQMwgHZ2nagFKG6ivz9Shgszz5Gbje9o2S+jcg3wIYZ7s0YSrrlmMA2w9KWl3SlsBWwN9sv1zQq7ZFVY7qq9S8p43Uf/K/3yA7OLbflDQWeKOwSvKGpE8BO5FWJ8ZKOsn26A62v9QWlaRtGqmkQVuqPYcl5Lbn5BW4rzfWBSCN04Ekx7bcyaz0+5pYKN8beJ7k4MxsR5stxzGf/3679OOgvyAIioSDU0Ap59MXgYGSTIoxMekv6tlVYkJqyiWtD9wp6Su2/whMJ616/LGg+yngkcL1laQJcivauT1VYFvSikd777ktxw3tD3xF0imkSf8jktaw/XrWfT9/FpFXQ+4k9XcqaWtndCMN5zY/WcfmWcDGZXZUpAFbpgPbSlrB9vvZhhWAQRVs+AkwDrirkb6Qnt9E4DLb7+fYqaq/L0kn5PimfYC1gD2BayXdbHt+g20GQRAEBeItqiU5gBRTsont/rY3aVnY7wAAIABJREFUAuYAHQ5ysP0scBJQ+nN0FHCopMGwaNI7GyhGPo4BDiZNhkVHqCEkfYEUn9FQ3IgSR5NiV24iBTZPtr1RHodNgKtJ2yvV6thS0hYF0WDgHw22vxJpC+qpwkrXUuTJ/jfAzwpvG60v6eD22mJ7FvAwcGpBfCowMZcVdWeQHNB9GumP7SeBU0iOcZFqv68dJa0K/BQ4wvZU0jbhKY20FwRBECxNODhL0gZcWya7mhT82RmuA3pL2ik7PAcDv5Y0A7gPuMT2n0rKth8B5gO32y4PnTxHS74+XXo7aHi+fizbu3+dN6hKdU0GHgM+DQzNbwtVG4da2zSrA5cpvRY/hbTFMqJO+1dk3WmkgOV9C2WrlvXzrCw/lRT8+4ikaaSxLX/VplFbDgM+LmmWpNnAx7OsEmfQDkfX9q9szy4T1xrXHwDX5WdPtvfAMkctCIIgaBClF2iCIGhWhgwZ4gkTJnS3GUEQBMsESQ/ZHlJPL1ZwgiAIgiBoOSLIuMWRNIr0yneRC2xfuozavxbYtEx8ou2bl0X7HwTdPaZBEARBfcLBaXFsH9HN7Q/rzvY/CLp7TIMgCIL6hIMTBE3Ou8+9y7Mjy7NBtDbrn7BBfaUgCHo0EYMTBEEQBEHLEQ5OEARBEAQtRzg4QRAEQRC0HOHgLAdIeiP/21+SJR1VKLtQ0qH5+2cl3Z8PvntU0ghJ3yochveOpKllB+Mh6XpJ48vaHCHpuHbYOCzbNqAgK9n7o4JsHUnvZrtPKdi2sPD96CptjJD0TNaZJukrZeWTJY0pXB+ec2KVrteUNFvSppJGS5ovaY1C+QXZ3nXyddGmSZJOyvI7JU0o3Dcky/Ys6L4haWb+fnmV/uyS2zusINs2y47L16MlHVCr3RqPJQiCIKhCBBkvf8wFjpH0q3yqcJHLgK/ZnqyUu2nLfPLtpQCSniCdRvxC6QZJawPbAW9I2tT2nA7a1QbcQ8qRNaIg/zsphcEP8vVXSXmesH0G6QRgJL1RJWdXOefZPlfSVsDdkvrmfE5bkRzynSWtlk94/jVwiKTdbP8fcDrpVOg5SvmfZpFOR/6dUp6poUAxGnephJ4F+kra2/aNJUF+tf3m3J87geNs1zthbyownJRiAtL4Ta6hv1S7PY2zJozkhQXlh1MvSa9p9f/X1a9fP0aOHFlXLwiC1iQcnOWPecC9pOSQ5bmk+gLPwqJkko9Qn/2BP5EyVB9IyvnULiStTjr3ZSgpN9aIQvEC4FFJQ/JkPxy4Cvhoe9spYvtRSe8B65Ccvq8DvyUlIP0KMCYnqPwu8Pu8yrUrKXFpiTHZnt8Bu5DGde8GTTiHlBais47Gk8CaktbL/dgL+Etn25V0OCnfGBus3VpvFL2wYB7PzX++tlKkIA2CoA7h4CyfnAXcKOmSMvl5wMy8enATKVv1W3XqagNOIzk44+iAg0NKsnmT7cckvSRpO9sTC+VXkvImPQcsBP5JJx0cSduTspWX/pQfDuwObAkcSc6ybnuKpJuB24D9yla9Hgf2ldSHNA6/Y0kHZ1VJkwrXZ9oubXmNB4ZJGgrUzFzeAONIK1sPk7KMv11Dt6F2bV8EXAQwaMNBLZVvZZ1V162r06tPYys4QRD0XMLBWQ7JWywPUJbc0vbpkq4A9shlbaSViYrkVYPNgXvyasd7kgbantZOk9qA8/P3K/N10cG5CfgRyYkaS+c4Vik7+OvA8Gz3p4F5tv8h6WngEkl9bL+c7xkF7G37jgr1XUNaudoe+E5ZWa0tKoAfk1ZTTuxMh0grWmOBASTHbIc6+l3VblNy0pAT6urEOThBENQjgoyXX35CmuCWeEa2Z9v+JWk7ZpCkj9SoYzjQB5iT43P6kyb7hsn1fxG4ONdxPClzuQo2vQM8BPwXKTt2ZzjP9mDbO9m+O8vagAG5/dnAmqSttxLv508lriQ5X7farqZTEdu3A6sAn23PfRXqeQ54l7QCdduyajcIgqAnEw7OcortGaQYm31KMklfLjgWW5C2g16pUU0bsJft/rb7k+JT2uXgAAcAl9veJNezETAH2LFM76ekHFMvtrP+muTg4K8C2xT6sS+pb3Wx/SRwCvCLDppwBlB/SaE+PySNz8Jl3G4QBEGPJByc5ZszgA0L198gxeBMIgXcHlRtwpTUH9gY+FtJlt+gei3HtwCcKunp0qeKDW3AtWWyq1l6+2y67csa6lX72Bl4xnbx7ae7gK0lrd9IBbZ/ZXt2haJVy14TP6vCvX9hcRxQh7F9n+3r2qHfJe0GQRD0VGS3VHxiEPQ4Bm04yDcdXevFrNYjYnCCoOci6SHbQ+rpRZBxEDQ5K/VbKSb8IAiCMsLBCYBFwcSVAmB3/QDiak4hxdUU+UM+GLDpkLQncHaZeI7tYd1hTxAEQRBbVEHQ9AwZMsQTJtQ7UDkIgqA1iC2qIOghvPv8Wzz30xndbcYyp99/DaivFARBjyXeogqCIAiCoOUIBycIgiAIgpYjHJwgCIIgCFqOcHDqIGmYJEsakK/7S1qQD4Z7RNL/SlqhHfLLJa1UqH9HSQ9ImpE/h2f5LpLGl9myoqTnJa0vabSkOYVD6u7LOodKmifpYUmPS7pZUr3cR6W6X5B0Zpn8TklPFlMzSLpO0huSPllo/6WCPf9Xp61jJb0laa2CbJc8zv9SkN0gaZeCHRMKZUNy0tFSny+sYPeQ/P0JSesVbH1O0jP5+2RJ90nau3Dv1yTdVMN+S/pt2djNk3RDwZ55ZYcIbt2Z/gdBEATtIxyc+rQB97BkioPZOUnjNsDWpGzbjcg/STqZ+GsAkvoBvwf+3fYAUvqD70j6Mum03g3zicQldgOm2X42Xx+f8zYNtl10Ysba3tb2FqTM5NdI2qpOP/cAZgJfKzozmVeAz2eb1wbWB7A9tdQ+8MeCPbvVaasNeBAof436aVJahWr0LToi7WRhwdb/ZXHOq0HA4cD/SFpF0mqkE6SPqFHXm8BASavm692BZ8p0xhaezWDbjxTKOtr/IAiCoEHiLaoaSFqdNLEPJU3gI4rltt/LKyebU8iuXUO+UClLeOlUtiOA0bYn5vIXJJ0AjLD9Z0l/ICXMLJ2xciApG3XD2L5D0kWkSfzYGqptwAXAd0lJHourR1fmtu8B/pWUofsT7bGjhKTNgNVJSTtPBkYXiicDK0na3fatFW4/h5Rl+8aOtF0N29Mk/YmU3HQ1Uu6tSqkditwIfBkYRxq7McBO9drqZP97FGc+8HNemP9S1fJek1eqWgbQr18/Ro4c2dVmBUHQJISDU5v9gJtsP5a3YLYDFv0fV1JvUlbvHxZvqiFfBdgeOCaLPgGU52+awGLnYQxwEXC2pJWBL7Gkk3KOpFPz9+m2D6rSj4nAd6p1Mq9E7Jp11iZN2EUH5zbg15J6kRydw4EfVKuvDiVn4G5gS0l9bc8tlP84fypN8OOBYZKGAq93sP1qnEYap3eAuucrkJy+H+ZtqW2AS1jSwRkuqZiQ9HO2F9C5/i8ib2UeDrBBn482YG7z8cL8l3hu/tzqCvOXnS1BEDQf4eDUpg04P3+/Ml+PAjZTSnhp4HrbN+atpHryLYBxtqfkOpV1yzGA7QclrS5pS2Ar4G+2Xy7oHW97XAP9KN9yKmcf4A7b8yVdDfxA0rGFRJ4LSas3w4FVbT+x9C5WwxwIDLP9vqRrSCcajyoV2r5bEpKqrYb8mLSKc2JBVu20yoZPsbT9pqSxwBu2325Af0p+tm1ApURQY20fWUHe2f6X9C4iOb8M2mhgS57WuU7vD9cs79Wn/gpOEAQ9l3BwqqCUuuCLpFgLA71IE+YvWBxTU05NuVL26zslfcX2H4HppNWCPxZ0PwUU4zVK20Nb0c7tqQLbAo/WKG8DPi/piXz9EdK2XDFY+EpSVvERHbQBSduQnLxbs4P0IeDvFCb4zBmkWJT3yuuwfbukH5G20Uq8CPQpU/0w8EI7TXw/fxrlj8C5wC6kMatJV/S/J/H9zxxVszwO+guCoBYRZFydA0ixGJvY7m97I2AOKUi4Q+Tg4JOA72fRKOBQSYNhkVN1NlAMHBgDHExytoqOUENI+gJpK+PXVcrXJAU3b5z72Z8UG9RWpno3cCYdd7LIdY4otWP7o8AGkjYpKtm+heSwDKpSzxnACYXrB0kOWr/cpyHAysBTnbC1ES4BTrc9tUH9rup/EARBUIdwcKrTRlqxKHI1KTC0M1wH9Ja0U3Z4DibFt8wA7gMusf2nknJ++2Y+cLvtN8vqOqfsVeQPZfnwfP1Ytnd/29VWcP41113clrke+EqO+ynZYdvn2m7vqkiRA1l6TK9lyTfUSpxBFWfS9l+AeYXr50lxTX/JW4HnA22227Ma025sP237girFw8uezQ50Uf+DIAiC+kSyzSBocgZtNNA3f6+RUKzWIraogqBnoki2GQQ9g5XWWyUm+yAIgjLCwelBSBpFPrCvwAW2L+3idj4J/LZM/Lbt7buynQ+SHA91W4WiXW2/uKztCYIgCNpHODg9CNu1TuftynamApXeJmsashPT1H0IgiDoyYSDEwRNzrvPz+f58x/qbjO6jfW+96nuNiEIguWQeIsqCIIgCIKWIxycIAiCIAhajnBwgiAIgiBoOcLBCYIgCIKg5WhqB0fSMEmWNKAg+7ikv0iaJelRSVdJKp4q+4akmfn75ZJ2kXSDpP6Snpa0QlkbkyR9RtIISc+UnU67dhW7dpH0qqSHc1t3Sdqngt5kSWMK14fnhI+l6zUlzZa0qaTPSro/t/uopBF1xmZvSROy7gxJ52Z5sR+PSGor3DNa0pxC/+7L8kMlzcv9eVzSzflk3uJ9B0i6Nt83K/e/eIpvJRufkLRO2bjdUNZmyc5vN2JL1llR0guSzizIqtom6c7Cb2KSpKqn5uXxs6TNC7Jjs2xIoV9TC/X9rJZtWX6npAmF6yGS7qxmRxAEQVCbZn+Lqo2U5fpAYISkVYA/A/9ZSncgaSgwr5QEM08ax9mekK93AcgZsp8CdgL+mssGAGvYfkDSl4DzbJ/boG13294n1zMYuE7SAtu3ZdlWJAdzZ0mr5TQMvwYOkbSb7f8DTielbpgj6Sbga7YnS+oFbFmtYUkDgQuBL9ueIWlFUj6qEufZPlfSFsBDksbZfjeXVctQvig7dh7TayQNLaaAsD2sMKbHlfrfCcbaPlJSX2C6pD8W5TVs2QOYCXxN0sk5zURV25QSXx5U+k00wFTSb+7H+foAlkyQCjC0SlqLpWwrlPWVtLftGxu0o+U5c/yvmbfg5Zo6vSauXLO8X79+jBw5sqZOEAStR9M6OJJWJx1aN5SUhHIE8HVgfFkupzvaUe0Y0sT113x9IJ1LLlmyYZKk04EjWXx43NdJh+FtBXwFGGPbkr4L/F7SocCupOziAH2BZ3N9C1l6Qi1yAnCG7RlZ/z1SFvRyux6XNJ+U2HFuO/pzh6SLSE7TsY3e11Fsz5U0G9ikQlklW9qAC4DvkrKOj+9ik64D9gV+LOljwKvAu7VvWUQt284BTgXqOjiSDic7rRv26dew4c3GvAUv89ybddKflWdoC4IgoIkdHGA/4Cbbj0l6SdJ2wECgMweCXAU8LOmo7BQMB75aKD9W0sH5+8u2h7aj7onA8YXr4cDupJWYI8mOlO0pkm4mOUL72X4n658HzMwrUDcBl9l+q0pbA4Gf1jMoj9njtovOzTmSTs3fp9s+qEZ/vlOvja4gOxEfA2YBW9eyRdKqJMfwO8DaJIeiEQfnCkkL8vdbbR9fQ/c14Km8UrYvMBb4VpnOHZIW5u+X2T6vAdvGA8PyqtTrtYy1fRFwEcCgjbZu2YRy667ap65Or7Xrr+AEQdDzaGYHp42UNRrgynzdKWw/J2k6sKuk54F3bU8rqLRni6ocLfoifZq0bfYPSU8Dl0jqY7u0Fj8K2Lu4+mT7dElXkLY4vk7q7y4dtOXYHNPyMWCvsrJqW1RV+9MJKk3MRdlwSTsCbwPfsf1S3k6qZcs+wB2250u6GviBpGPzqlct2rNFBek3dyCwJ8lpKXdwKm1RNWLbj0mrOCe2w5aW5fuf+3ZdnTjoLwiCSjRlkLFSnqAvAhdLeoK0MjIcmM7iLZ2OUtqm6pLtqQLbAqUYkTZgQLZ9NrAmsH9B9/38WQLbs23/kjShDsrjUIl643Ce7S1JY3Z5jl1qL8X+dJQXSdtjJT4MFJ2CsbYH297e9rUN2tIG7JbH9iHgI6RtzK7mT8A3gCdtv9bgPXVts307sApp+yoIgiDoIE3p4JCCOi+3vYnt/rY3AuYAjwE7SPpySVHSXkrJHxvlauBLpMn/yq4wVtI2wA+AUUpvaX0V2Cbb3p+0zVFzBUrSl7V4+WILYCHwShX1c4CTJX0837uCpP8sV7J9DTABOKSd/fkCKf7j1+25rwJ3kpwEcuD0wUB7YqaWsEXSmsCOwMaFsT2CLljdK8f2AtIqyxkN2tke284gxVEFQRAEHaRZt6jagLPKZFeTtm72Ac6XdD4p8HMKcEyjFdt+RdLfgPVszykrLsbgQIqReaJKVTtJehjoTQrgPdr2bfktnmdsP1PQvQvYWtL6tp+tUt83gPNyUPB7pC2VitsuOY7ne8AYSb1J2z5/rlLv6aSg5pKzUozBAfhM/re0XdSb5EzuX3yDqoP8CPilpMmkbaabgN81cF9FW3Jg9u223y7oXg+MlLRymbycYgzOC7Z3q2eE7VoOcDEGZwpwey3byur9i6R59doPgiAIqqMl31INgqDZGDJkiCdMaE/4UBAEQfMi6SHbQ+rpNesWVRAEQRAEQVWadYtquUDSnsDZZeI5pUPllkH732Lp7bd7bR+xLNpvFEnXApuWiU+0fXN32NMIkk5hySMCAP5gu6GYmyAIgqB7iS2qIGhyYosqCIKeRKNbVLGCEwRNzrtz3+D5n93d3WZ0G+sdvVN3mxAEwXJIxOAEQRAEQdByhIMTBEEQBEHLEQ5OEARBEAQtR8s5OJIWSpokabKkiZJ2yPJdJN1Qpjta0gH5+52SZub7HpQ0uKC3lqTLJc3On8slrVXDhv6SFkh6WNKjkh6QtNRpwZKulzS+cL2HpPGlE4sl9cp92UHSltnGSbnOi+qMw2ck3ZX7NEPSxZJ6SzpU0rxczwxJxxbuGSHpmVxW+qydx+7V3J+Zud59yu47TtKofM8juf+lOg6oYuNoSXMKevdl+aGS3s8nQJd0p0nqn78/IWlq/jwi6celw/I6MfanFOxYWPh+dLVxqdKnXSRZ0mEF2bZZdlxBtqKkFySdWZD1kvSQpJ0Lslsklb/NFQRBENShFYOMF9geDIte4z4T+EKD9x5ke0J+/focUrZvgN8A02x/M9d7GnAxS79GXGS27W2z/seAayStYPvSLFsb2A54Q9KmtufYvkXS/wMOy/UfBTxo+z6lDOPn2b4+3181/YSk9YA/AAfaLjlM+wNrZJWxto9UymU1U9I420/lsqUSimZ/627b++TrwcB1khbYvq2kV3o9PTsiN5SeQx2qJfd8GjiFlDKjEkNtvyBpdVJW7YtYnHKiI2N/BjntgqQ3irZLGlFpXGowNdv9m3x9IDC5TGcPYCbwNUknO7FQ0n+QcqxtR0pJYtt/aLDdluPM+y5n3vxqGUkSvSZUzxjSr18/Ro4c2dVmBUHQBLSig1NkTeDlulpLM56UwBNJm5MSVxYn2tOBWZI2sz27XmW2/66UC+qnwKVZvD8pYePzpAmw9Jf8scA9eXXhSBanSlifNOmX6pxao8kjgMtsj8+6Bsbl/hTtelHSrFz3UxXqqdafSZJOz/bdVk+/g9wA7CxpS9sza9jyhqR/B56S9OEK5e0Z+67iSWDN7GjOJWVs/0uZThtwAfBdUmLN0rO6P69kjSClHtmdCkg6nJSDiw37rNfF5i8/zJv/Cs+9+VJtpTeXjS1BEDQXrejgrCppEikj8/qkrOPtZS/guvx9a2BSMe9T/kt7EvAJUjbwRpgIDChctwGnkSbZceRJ1vazSnm0xpPyV5X+734ecHue/G4BLrVd7U/bgcBl9QyStDFpnKYUxMV8Wy/brpaJeyLZCewkxdxX020flL+/D4wETqZOMlDbr0maQ0pC+nwVW+uOfR0aHZcS40grfA/n9hfloJK0Kikj/HeAtbM94wv3fp/kcJ5ve1alym2XVq0YtPGAlj3Mat3eFXcCl6DX2qtWLevXr19XmhMEQRPRig5OcYvqc8DlkgaSEk5Woii/QtJqQC/SFgakJJCV7q0mr8aipZP8l/3mwD22Lek9SQNtT8sqo4CzbI9eZKR9ad6m2ouUffw7kgbVSSBZjeGShgJbAt+2/VahrNGtGNVXaYhqW1QAvwdOkVR+CnJ77WnP2FejPVtUAFcBY0mO1Rhgh0LZPsAdtudLuhr4gaRjC070zsCrJEe1R/P9Hb5ZVyfOwQmCoBItF2RcJG/RrAOsC7wI9ClT+TDwQuH6IFJKgd+TnAyA6cC2khaNVf4+CGhPNu1tC/rDsy1zJD0B9CdtlZTsfp8KzpPtf9q+xPa+pIzi1SbA6aRttWqMtf0JYCfgp5I68mdusT8fCLbfI20tnVhLT9IapDF8rIpKw2PfVdh+jpTNfneW3sZrA3bL7T8EfAQYCpAd7JGklcd1JX2pq20LgiDoCbS0gyNpAGk15kXgceCjkrbKZZuQnJRJxXtsvwucCnxW0lZ5i+DhLCtxKjCx2vZBBTv6A+cCP8+iNmAv2/1t9yc5IzUnWUl7SVopf+9HmhSfqaJ+IXCIpO0L9x9c7shkB/C3LJ3Pql5/tgF+wGIn8INkNLAbyUmtZMvqwC+A62wvFW/VFWPfCX5Iyrm1aHtT0prAjsDGBRuOyHaV7rnK9gzgP4DzJK3yAdkXBEHQsrTiFlUpBgfS1sQheYJZmGMoLs0TxrvAv9l+tbwC2wsk/RQ4jvRG02HAz3NArkjxEoeV31fGZpIeJsW4vA78PG8z9Qc2Bv5WaG+OpNckbW/7/ir17QFcIKm0nXR8XiVYCtvPSzoQOFdSX1I8y13ANRXUzwYmSvpJvi7GmgDsl//dKfenNylw9ujiG1SdoBiDA4uDqkt9eUfSz0gBuUXuyG+HrQBcC/yoUNbVYw8VxsX2E7U6Zvu+CuJ/BW4v21q8HhgpaRAwjOR4l4K5byatYJ1Wq60gCIJgSSLZZhA0OYM2HuBbjqv+qnSrEzE4QdCzUCTbDIKewUp9V49JPgiCoIxwcDpBPmzvt2Xit21vX0n/A2h/T9IWU5E5tocti/YbRdIo4PNl4gtKB+81I80y9kEQBD2V2KIKgiZnyJAhnjBhQnebEQRBsEyILaog6CG8N/c15l54S3eb0S30PXKP7jYhCILllJZ+TTwIgiAIgp5JODhBEARBELQc4eAEQRAEQdByNKWDI2mhpEmSJkuaKGmHLN9F0g1luqMlHZC/3ylpZr7vQUmDC3prSbpc0uz8uVzSWg3Ycqykt4q62Q5LOqwg2zbLjpM0Ktv/iKQF+fukkp1V2jlO0gxJ07L932ygT09Imlqo/2eFMZmT73ks93WDsvvWK9z3nKRnCtcfqmLjKZKmS5qS9bYv1LdO2fjckL8fmsdl10L5sCyrNR6lfk/J43KhpLUL5QsL9k6SdFLZfZMkPaqUlRtJ92fZk5LmFe7rX6X9JyTdXSabJGlaoY+vltmwW4U+DijI+mfZUQXZhZIOrTYOQRAEQWWaNci4mFBzT1I26C80eO9BtidI+hZwDilXEMBvgGm2S47DacDFpIzQtWgDHiSdQDu6IJ9Kynv0m3x9IDAZwPYRuY3+wA2lvlRD0r9nOz+TM2evxeIThmv1CWCo7WK+rRLH2x4nScD3SCcDD7T9Ti5fWBjjEcAbtZJNKiU23QfYzvbb2aGp6AhVYCppHEsnIy8aqzqU+v0h0m/gehb/DhbUGNfSfR8GZksaXXq1PzsTQ2wf2UD7a0jayPZTyilAyrjb9j5V7m0D7iH1dURBPhc4RtKvCs+iR/OTe8cy782lDhwHoNcDv6t6X79+/Rg5cuQHZVYQBMs5zergFFkTWCoHUQOMB44HkLQ5KSfR8EL56cAsSZvZnl2pAkmbAavnek5mSQfnSWBNpezVc0lZwP/SATvJdQ+1/RpATi9xWa0+NYrTOQHnSRoG7E1yEjrC+sALpRQEVZyqatxNSgWxErAyKdv3pNq3LCancziB9LwG2W7EOYL07N4EFtZTrMJVpN/MuSSHZQzwjXo3KeXP+jwpweYfWdLBmQfcCxwCVD2eOK88HQ6wYZ++HTK+WZj35qs892aV/8SryYMg6PE0q4NTyje1Cmli/WIH6tgLuC5/3xqYVEyKaHthbuMTQEUHh8WT2t3AlpL62p5bKB9HWgF6GJgIvL10FbVRypS9RjUnq4xin0rcIanUr8tsn1fl3onAADru4NwC/FDSY8D/kTKW/7XBe53v2RNYizTpb9qexvPzmkzqw2SWzEkGcKbtsfn7FZLeBrYAvld87u1kHMmpPRf4F1I2+qKDs1OZDfvn57gfcJPtxyS9JGk72xMLemcBN0q6pFrDti8CLgIYvPHHW/owq3VXq75T3Gvt3lXL+vXrV7UsCILWp1kdnOIW1eeAyyUNJE2UlSjKr5C0GinL+HZZpir3VpOXOBAYZvt9SdeQnJlihu2rgLGkSXcMsEPNXlWmng1QuU8lqm1RVWqnw9h+Q9KngJ1IKxNjJZ1kezSV7S+XXQkcTXJw/ou0atVein1oZItqXeA+STfZ/kcH2nsJeFkpsemjwPyy8mpbVG3A+fn7lfl6kYOTE4A+AHy9Aza1HCd/fnjVsjgHJwiCajRlkHER2+OBdYB1gReBPmUqHwaKE/xBpNWB37PYGZkObCtp0Xjk74NIE9dSSNqGtAJwq6QnSM5OW5ltz5Gylu/O4viSdpG3pd6U9LEaapX61F62pUpfG8X2Qtt32v5v4Ehg/1xU/lzKnwkD6aA1AAAQ7ElEQVS2HwAGAuvYfqy9bUvqBXySdvTB9jySY9GZ1BpjSWM+phFlSR8hrThenH83xwPDcyxUkZ+Qsog3/X+jQRAE3UHT/88zv4XSizSJPg58tBTwKWkTkpOyRDyH7XeBU4HPStrK9izSNtKpBbVTgYm5rBJtwAjb/fPno8AGuc0iPwRO7MQ2CKQA2lGS1sz9WrP09k+1PjVasRJHk7b6buqogZK2lLRFQTQYKK2K3EneusmOyMHAHRWq+T4dWLnJsTtnAk/ZntKO+3qTHLtGtv+qcS0wEri5Qf0DgMttb5J/NxsBc4Adi0q2ZwCPkAK3gyAIgnbSrFtUxfgKAYdkB2KhpIOBSyWtQlo9+bcclLsEthdI+ilwHHBY/vxc0qxc5/gsq8aBpKDcItdm+f2Fdu7rSAfL+CUpIPZBSe+S+vXTcqUKfYIlY3CmlN4SA86R9AOgN/A30lZWZ97aWZ00fmsD7wGzyEGwwI+AX+YYGZEcqaVef7F9YzvbLMXSrEyK4dm3UFYeg3OT7ZMK9y3I9422/VA72y3a/Do56ebSizBLxeD8mOQYn1WmdzVpO6o8eecZJMc7CIIgaCeRbDMImpzBG3/ct5xwYXeb0S1EDE4Q9DwUyTaDoGewYt81Y6IPgiAoIxycOkj6JPDbMvHbpYPhuritUaTzUYpcYPvSrm6ro+Qg2UoB07vafvEDaO9aln5l/ETbjca8dLb9+0lbWUW+YXvqsmg/CIIg6Bjh4NQhT2Q1TxruwraOWBbtdIbsxCyT8cjtDVtWbVVpv8sd2SAIguCDJxycIGhy3pv7CnNHlZ/v2Pr0PWK/+kpBEPRYmv418SAIgiAIgnLCwQmCIAiCoOUIBycIgiAIgpaj6RwcSW/kf/tLsqSjCmUXSjo0f/+spPslTZL0qKQRkr6VrydJekfS1Pz9rEId10saX9bmCEnHNWjfwlzndEmTJf1nMQVE1rlA0jMluaRPSHpM0qoFnT9LOlDSepJuyHU9IqlqRvI8JgsKfZwk6Zu57AlJVxd0D5A0utaYSDpU0rx8PUPSsfnePSSNL6UXkNQr61TMtZXH75msM03SV8rKJ0saU7g+XNLYwvWakmZL2jTbPF8pCWlxPC1pnbJnUPqclOV3SppQuG9Ilu1Z0H1D0sz8/fIq/dklt3dYQbZtlh2Xr0dLmlOo976yOqr9zuZL6luQvVHJhiAIgqA2TefglDEXOEbShyqUXQYcnhMuDgSusn2p7cFZ9k/S6b2DSyfcKp3Cux2wtqR2ZbMusCDX+QlSDqovAf9dKsxOzTDgKWBnANvTgWuAU7LOfsBKtq8ETgdutT3I9tbASdRmdqmP+VOcpIdI+kRRud6YkLKCDya9vn6KpI1s30JKw1Ca4I8CHqxzavN5uZ6vApcUnLutSL/DnZUShgL8GthQ0m75+nTgEttz8vUs8qnFuZ6hwDOFthaUjUHx5OC+kpY4gdr2zYUxmEBKxjm4cOpzJaYCxSyQB5KymBc5vmDDIuevzu/sBVKy0SAIgqATNPtbVPOAe4FDSJNikb7As5CSQJLy+tRjf+BPwPOkCevMzhhne65SzqgHJY1wOjZ6KDCNlKSxjZSnCdIk/rCkcaSj/P8ly9cHbinU2XCupQqcS8r1dFB7b7T9olIai/VJztmxwD15FeJI4DMN1vOopPdICVLnklIU/BbYCvgKMMa2JX0X+L3SityuwKcK1YwhORe/A3Yh/QbK02ZU4xxSzq72poUo50lgTUnr5X7sBVRdXSuj1u/sEuBQSWfbfqmTNjY1P7nnOubNf71qea/7r6l5f79+/Rg5cmRXmxUEQZPQ7A4OJGfgRkmXlMnPA2ZKupOU++gy22/VqasNOI008Yyjkw4OgO2/51WGvrneNtIEfT3wE0kr2X7X9vy8vXEX8D+2H89VjALGSjqSlG/pUtv/rNHkZloy/9FRtu/O368C/kPS5u3th6SNgVWAKblfz0o6n5Sz6+hGJ2NJ2wPvk5xTSI7K7sCWJEdpTK5/iqSbSYcK7leWJ+txYF9JfUjj+TuWdHDK81Cdabu05TUeGCZpKFB99myMcaQVqYdJWcnfLis/R1Ipget02yXHstbv7A2Sk3MMhZW/crLjfDjAhn3W7VwvllPmzX+d5954pbpCrbIgCHo8Te/g2J4j6QHSSkBRfrqkK4A9clkb6a/9iuS/xDcH7skrCO9JGmh7WheYWYpV+RBpy+pY268rnZK7B/DnbPOfJL0C/KLQj5slfYy0QrA3aZVnoO155Y1kZuetlkosJK1gfJ/GVzCGZ2dgS+DbZU7iKOAs26MbqOdYpUSorwPD8xh/Gphn+x+SniZtXfWx/XKh/r1tV8o8fg1p9WN74DtlZQtqjAGkpJenAic2YHctriKtxA0gOWblMUjH2x5XFDT4O/sZMEkpcWpFbF8EXAQweOPNWzKh3Lq916hZ3mut1WqW9+vXryvNCYKgyWh6ByfzE9JfwncVhbZnk7JY/xqYJ+kjNdIJDAf6AHNy7OyapAn01Cr6DZGdk4WkbYx/AdYCpuY2egPzyQ5O5v38KfbjJeD3pC2bG0ixO1fTMX5LcnCmN6g/1vaRkj4H/FnSjbafy3a9L6nRyfU82+eWydqAAZKeyNdrkrZvLs7XS41FgStJqyaXZTsaNANs3y7pR8BnG76pcj3PKWV335204lIxyLqMur8z269I+j3wH52xr9k5ecfaB/nFQX9BENSi2YOMAbA9gxRjs09JJunLWjzrbUFyMmqtabcBe9nub7s/KebjwM7YJWld4H+BC3P8TRvwb4U2NgX2kNS7Rh1fLJXnN4c2I8V/dAjb75K2777XzvvGk5yjYzradpG8bfdVYJvCeOxLGqNG7HmSFJT9i3q6VTgDOKGD9xb5ISk31sIG9Rv9nf0PaWWqVf4ICYIgWKa0hIOTOQPYsHD9DVIMziTSxHxQtUlIUn9gY+BvJVl+Y+e1HDMCcKqkp0ufGnasml8Lnk6KmbkFOC07KXtSWK2x/SZwD4sDiivxKWCCpCmk+JGLbT9YQ38zLfmK9NEVdH5DxybOs4FvqfCKdifYGXjGdvHtp7uArSWt30gFtn+VV+nKWbVsDM6qcO9fWBwH1GFs32e7Wp6Ec8rs+Dj1f2cl+QvAtSyd6DMIgiBoAKWFhSAImpXBG2/uW04s3/1rfWKLKgh6JpIesj2knl4sfwdBk7Ni37Vjsg+CICgjHJwOIOkjpNeXy9m1RhBzV7b/SdK2W5G3bW9fSX9ZIekUUlxNkT/YPqM77OkskvYkbcsVmWN7WHfYEwRBEDRObFEFQZMj6XVgZnfbsQxZh3Tic0+gJ/UVelZ/e1JfoWv7u4ntugeAxQpOEDQ/MxvZj24VJE3oKf3tSX2FntXfntRX6J7+ttJbVEEQBEEQBEA4OEEQBEEQtCDh4ARB83NRdxuwjOlJ/e1JfYWe1d+e1Ffohv5GkHEQBEEQBC1HrOAEQRAEQdByhIMTBEEQBEHLEQ5OEDQpkvaSNFPSLEkndbc9XYGkjSTdIelRSdMlHZPlH5Z0q6TH8799slySfpbHYIqk7bq3B+1HUi9JD0u6IV9vKun+3Nexkj6U5Svn61m5vH932t0RJK0taZykGfkZf67Fn+2x+Xc8TdIYSau00vOVdImkuZKmFWTtfp6SDsn6j0s6pKvsCwcnCJoQSb2AUcDewNZAm6Stu9eqLuE94L9sbwV8Fjgi9+sk4DbbW5BOES85dHsDW+TP4cAvl73JneYY4NHC9dnAebmvLwOHZflhwMu2NwfOY+lTtpuBC4CbbA8ABpH63ZLPVtIGwNHAENsDgV7AgbTW8x0N7FUma9fzlPRh4L+B7YHPAP9dcoo6Szg4QdCcfAaYZfvvtt8BrgT27WabOo3tZ21PzN9fJ02AG5D6dllWuwwoJd/aF7jcib8BazeajX55QNKGwJeBi/O1gC8C47JKeV9LYzAO2DXrNwWS1gR2Bn4DYPsd26/Qos82syKwqqQVgd7As7TQ87V9F/BSmbi9z3NP4FbbL9l+GbiVpZ2mDhEOThA0JxsATxWun86yliEv0W8L3A+sZ/tZSE4Q0DerNfs4nA+cALyfrz8CvGL7vXxd7M+ivubyV7N+s/AxYB5wad6Su1jSarTos7X9DHAu8CTJsXkVeIjWfb4l2vs8P7DnHA5OEDQnlf6ya5kzHyStDlwNfM/2a7VUK8iaYhwk7QPMtf1QUVxB1Q2UNQMrAtsBv7S9LfAmi7cvKtHU/c3bLPsCmwIfBVYjbdOU0yrPtx7V+veB9TscnCBoTp4GNipcbwj8s5ts6VIkrURybq6wfU0WP1/ansj/zs3yZh6HzwNfkfQEaYvxi6QVnbXzlgYs2Z9Ffc3la7H09sDyzNPA07bvz9fjSA5PKz5bgN2AObbn2X4XuAbYgdZ9viXa+zw/sOccDk4QNCcPAlvkNzI+RApe/GM329RpcszBb4BHbf9PoeiPQOntikOA6wvyb+Y3ND4LvFpaHl/esf192xva7k96frfbPgi4Azggq5X3tTQGB2T9pvkL3/ZzwFOStsyiXYFHaMFnm3kS+Kyk3vl3XepvSz7fAu19njcDe0jqk1e99siyzmM7PvGJTxN+gC8BjwGzgVO6254u6tOOpOXpKcCk/PkSKRbhNuDx/O+Hs75Ib5PNBqaS3ljp9n50oN+7ADfk7x8DHgBmAX8AVs7yVfL1rFz+se62uwP9HAxMyM/3OqBPKz9b4DRgBjAN+C2wcis9X2AMKb7oXdJKzGEdeZ7A/8v9ngV8q6vsi1QNQRAEQRC0HLFFFQRBEARByxEOThAEQRAELUc4OEEQBEEQtBzh4ARBEARB0HKEgxMEQRAEQcsRDk4QBEGLIOm+Zdxef0lfX5ZtBkGjhIMTBEHQItjeYVm1lU/b7Q+EgxMsl8Q5OEEQBC2CpDdsry5pF9Ihc8+TDte7hnS42jHAqsB+tmdLGg28BXwCWA/4T9s3SFoF+CUwBHgvy++QdCgp+/kqpNxKvYGtgDmkzNHXkg60Wy2bdKTt+7I9I4AXgIGkpJMH27akTwMX5HveJp34Ox84i3QA4srAKNu/6uLhClqcFeurBEEQBE3IIJLz8RLwd+Bi25+RdAxwFPC9rNcf+AKwGXCHpM2BIwBsf1LSAOAWSR/P+p8DtrH9UnZcjrO9D4Ck3sDutt+StAXppNsh+b5tSY7UP4F7gc9LegAYCwy3/aCkNYEFpBNxX7X9aUkrA/dKusX2nA9gnIIWJRycIAiC1uRB59xNkmYDt2T5VGBoQe8q2+8Dj0v6OzCAlDLj5wC2Z0j6B1BycG61XS0J5ErAhZIGAwsL9wA8YPvpbM8kkmP1KvCs7QdzW6/l8j2AbSSVcjatBWxBWikKgoYIBycIgqA1ebvw/f3C9fss+f/+8jgFk/IGVePNGmXHkrbFBpFiPN+qYs/CbIMqtE+WH2W7a5IuBj2SCDIOgiDo2XxV0gqSNiMlgpwJ3AUcBJC3pjbO8nJeB9YoXK9FWpF5H/gG0KtO2zOAj+Y4HCStkYOXbwa+K2mlkg2SVqtRTxAsRazgBEEQ9GxmAn8lBRn/e46f+QXwv5KmkoKMD7X9trTUws4U4D1Jk4HRwC+AqyV9FbiD2qs92H5H0nDg55JWJcXf7AZcTNrCmqjU6Dxgv67obNBziLeogiAIeij5LaobbI/rbluCoKuJLaogCIIgCFqOWMEJgiAIgqDliBWcIAiCIAhajnBwgiAIgiBoOcLBCYIgCIKg5QgHJwiCIAiCliMcnCAIgiAIWo7/DwOFeM7BHhdoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "cols = feature_importance_df[[\"feature\", \"importance\"]].groupby(\"feature\").mean().sort_values(by=\"importance\", ascending=False)[:40].index\n",
    "best_features = feature_importance_df.loc[feature_importance_df.feature.isin(cols)]\n",
    "plt.figure(figsize=(8, 10))\n",
    "sns.barplot(x=\"importance\", y=\"feature\", data=best_features.sort_values(by=\"importance\", ascending=False))\n",
    "plt.title('LightGBM Features (avg over folds)')\n",
    "plt.tight_layout()\n",
    "plt.savefig('lgbm_importances01.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Below goes code that was used to find best parameters for our model. It was executed on Kaggle Kernel and not here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightgbm import LGBMClassifier\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold,train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_later, X_test, y_train_later, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=1)\n",
    "print(\"Postive examples in train set: {}\".format(np.sum(y_train_later==0)))\n",
    "print(\"Negative examples in train set: {}\".format(np.sum(y_train_later==1)))\n",
    "\n",
    "print(\"Postive examples in test set: {}\".format(np.sum(y_test==0)))\n",
    "print(\"Negative examples in test set: {}\".format(np.sum(y_test==1)))\n",
    "X_train_later=X_train_later[0:50000]\n",
    "y_train_later=y_train_later[0:50000]\n",
    "print(\"Postive examples in train set: {}\".format(np.sum(y_train_later==0)))\n",
    "print(\"Negative examples in train set: {}\".format(np.sum(y_train_later==1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n_estimators in range(100,3000,30):\n",
    "    for learning_rate in np.arange(0.1,0.001,-0.05):\n",
    "        for num_leaves in range(2,100,30):\n",
    "            for subsample in np.arange(0.1,0.9,0.1):\n",
    "                for max_depth in range(4,30,7):\n",
    "                    for min_child_weight in range(0,5,1):\n",
    "                        clf007 = LGBMClassifier(n_estimators=n_estimators,learning_rate=learning_rate,num_leaves=num_leaves,colsample_bytree=0.8,subsample=subsample,max_depth=max_depth,reg_alpha=0.1,reg_lambda=0.1,min_split_gain=0.01,min_child_weight=min_child_weight)\n",
    "                        print(\"n_estimators=\",n_estimators,\"learning_rate=\",learning_rate,\"num_leaves=\",num_leaves,\"colsample_bytree=0.8,subsample\",subsample,\"max_depth=\",max_depth,\"reg_alpha=0.1,reg_lambda=0.1\",\"min_split_gain=0.01\",\"min_child_weight\",min_child_weight)\n",
    "                        clf007.fit(X_train_later,y_train_later)\n",
    "                        y_pred=clf007.predict(X_test)\n",
    "                        newdf=pd.DataFrame(data=y_pred)\n",
    "                        newdf.iloc[:,0].value_counts()\n",
    "                        print(classification_report(y_test, y_pred))\n",
    "                        print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf007.fit(X_train_later,y_train_later)\n",
    "y_pred=clf007.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "349e5c402e3026fe41cb1e9e6e963668eade0f8c"
   },
   "outputs": [],
   "source": [
    "newdf=pd.DataFrame(data=y_pred)\n",
    "newdf.iloc[:,0].value_counts()\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
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}