model_selection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_curve,auc\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import StratifiedKFold,RandomizedSearchCV,GridSearchCV\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import kds\n",
    "\n",
    "pd.set_option('display.max_rows', 999)\n",
    "pd.set_option('display.max_columns', 999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Julian\\Documents\\projs\\home-credit-default-risk\\data\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "%cd data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_cat_col_values(df):\n",
    "    cats = df.select_dtypes(include = ['object'])\n",
    "\n",
    "    for col in cats:\n",
    "        ratios = []\n",
    "        values = pd.DataFrame(df[col].value_counts())\n",
    "        for value in values[col].values:\n",
    "            total = np.sum(values[col].values)\n",
    "            ratios.append(value/total)\n",
    "\n",
    "        values['Ratio'] = ratios\n",
    "\n",
    "        print(values.sort_values(by = 'Ratio', ascending = False))\n",
    "        print(\"\\n\")\n",
    "\n",
    "def show_missing_values(df):\n",
    "    na_dict = {}\n",
    "    for col in df.columns:\n",
    "        na = df[col].isna().sum()\n",
    "        total = len(df[col])\n",
    "\n",
    "        na_dict[col] = f\"{round(100*na/total,2)}%\"\n",
    "\n",
    "    print(pd.Series(na_dict).sort_values(ascending = False))\n",
    "\n",
    "def remove_cat_col_values(df, threshold):\n",
    "    \"\"\"\n",
    "    If one single value dominates the entire column, then we remove the column entirely, as there is too little variation in the column values\n",
    "    \"\"\"\n",
    "    assert 0 <= threshold <= 1, \"threshold must be between 0 and 1\"\n",
    "    cats = df.select_dtypes(include = ['object'])\n",
    "    for col in cats:\n",
    "        print(col)\n",
    "        values = pd.DataFrame(df[col].value_counts())\n",
    "        for value in values[col].values:\n",
    "            total = np.sum(values[col].values)\n",
    "            if value/total >= threshold:\n",
    "                df = df.drop([col], axis = 1)\n",
    "    return df\n",
    "\n",
    "\n",
    "def remove_missing_values(df, threshold):\n",
    "    assert 0 <= threshold <= 1, \"threshold must be between 0 and 1\"\n",
    "\n",
    "    for col in df.columns:\n",
    "        na = df[col].isna().sum()\n",
    "        total = len(df[col])\n",
    "\n",
    "        if na/total > threshold:\n",
    "            df = df.drop([col], axis = 1)\n",
    "\n",
    "    return df\n",
    "\n",
    "def remove_bad_rows(df, col, value):\n",
    "    rows = df.loc[df[col] == value]\n",
    "    \n",
    "    df = df.drop(rows.index, axis = 0)\n",
    "    \n",
    "    return df\n",
    "\n",
    "def show_ratio(df, target, col):\n",
    "    dict_ = {}\n",
    "    for value in set(df[col].values):\n",
    "        \n",
    "        target_1 = df.loc[(df[target] == 1) & (df[col] == value)]\n",
    "        target_0 = df.loc[(df[target] == 0) & (df[col] == value)]\n",
    "\n",
    "        if len(target_1) == len(target_0) == 0:\n",
    "            dict_[value] = -1\n",
    "        elif len(target_0) == 0:\n",
    "            dict_[value] = -2\n",
    "        else:\n",
    "            dict_[value] = len(target_1)/len(target_0)\n",
    "    \n",
    "    series = pd.Series(dict_).sort_values(ascending = False)\n",
    "    series = series.replace({-1: \"no values found\", -2: f\"{value} has all target values of 1\"})\n",
    "    print(col)\n",
    "    print(series)\n",
    "    print(\"\\n\")\n",
    "\n",
    "def show_ratio_by_target(df, target, cols = None):\n",
    "    \"\"\"\n",
    "    This can take either a list, string, or all of the object dataframes in the dataframe\n",
    "    \"\"\"\n",
    "    if isinstance(cols, str):\n",
    "        show_ratio(df, target, cols)\n",
    "\n",
    "    elif isinstance(cols, list):\n",
    "        for col in cols:\n",
    "            if col != target:\n",
    "                show_ratio(df, target, col)\n",
    "\n",
    "    elif isinstance(df, pd.DataFrame):\n",
    "        for col in df.select_dtypes('object'):\n",
    "            if col != target:\n",
    "                show_ratio(df, target, col)\n",
    "\n",
    "    else:\n",
    "        return \"Invalid type for parameter col\"\n",
    "\n",
    "\n",
    "def show_unique_keys(df):\n",
    "    for col in df.columns:\n",
    "        print(col, df[col].is_unique)\n",
    "\n",
    "def get_cat_agg(df, col, type):\n",
    "    cat_agg = {}\n",
    "    for i in range(len(df.columns)):\n",
    "        if col in df.columns[i]:\n",
    "            cat_agg[df.columns[i]] = type\n",
    "    \n",
    "    return cat_agg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_evaluation(x, y, model, train = False):\n",
    "    # retrieve just the probabilities for the positive class\n",
    "    yprob = model.predict_proba(x)[:,1]\n",
    "\n",
    "    #retrieve the predicted class for each of the test set observations\n",
    "    ypred = model.predict(x)\n",
    "\n",
    "    auc_pr = metrics.average_precision_score(y,yprob)\n",
    "    acc_score = metrics.accuracy_score(y,ypred)\n",
    "    conf_mat = metrics.confusion_matrix(y,ypred)\n",
    "    rep = metrics.classification_report(y,ypred)\n",
    "    auc = roc_auc_score(y,yprob)\n",
    "\n",
    "    if train:\n",
    "        print('-----------------Train Set Evaluation -----------------')\n",
    "    else:\n",
    "        print('-----------------Test Set Evaluation -----------------')\n",
    "\n",
    "    print('Accuracy Score:  %0.4f \\n' % acc_score)\n",
    "    print('Area under ROC Curve:  %0.4f \\n' % auc)\n",
    "    print('Average Precision Score:  %0.4f \\n' % auc_pr)\n",
    "    print('Confusion Matrix:')\n",
    "    print(pd.DataFrame(conf_mat, columns = ['Positive Prediction', 'Negative Prediction'], index = ['Positive Class', 'Negative Class']))\n",
    "    print(\"\\n\")\n",
    "    print('Classification Report:')\n",
    "    print(rep)\n",
    "\n",
    "\n",
    "def show_plot_metrics(x_train, x_test, y_train, y_test, model):\n",
    "    show_auc_roc_plot(x_train, x_test, y_train, y_test, model)\n",
    "    show_pr_roc_plot(x_train, x_test, y_train, y_test, model)\n",
    "    show_lift_chart(x_train, x_test, y_train, y_test, model)\n",
    "    show_gains_chart(x_train, x_test, y_train, y_test, model)\n",
    "\n",
    "def show_auc_roc_plot(x_train, x_test, y_train, y_test, model):\n",
    "    plt.figure(figsize=(14,6))\n",
    "\n",
    "    yprob_test = model.predict_proba(x_test)[:,1]\n",
    "\n",
    "    ypred_test = model.predict(x_test)\n",
    "\n",
    "    fpr_test, tpr_test, thresholds_test = metrics.roc_curve(y_test, yprob_test)\n",
    "    auc_roc_test = metrics.roc_auc_score(y_test, yprob_test)\n",
    "\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.plot(fpr_test,tpr_test, color='orange',label='XGBoost (area = %0.2f)' % auc_roc_test)\n",
    "    plt.plot([0,1],[0,1],linestyle='--',color = 'blue',label='Random Model')\n",
    "    plt.legend()\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('AUC ROC Curve (Test Set)')\n",
    "\n",
    "    yprob_train = model.predict_proba(x_train)[:,1]\n",
    "\n",
    "    ypred_train = model.predict(x_train)\n",
    "\n",
    "    fpr_train, tpr_train, thresholds_train = metrics.roc_curve(y_train, yprob_train)\n",
    "    auc_roc_train = metrics.roc_auc_score(y_train, yprob_train)\n",
    "\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.plot(fpr_train,tpr_train, color='orange',label='XGBoost (area = %0.2f)' % auc_roc_train)\n",
    "    plt.plot([0,1],[0,1],linestyle='--',color = 'blue',label='Random Model')\n",
    "    plt.legend()\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('AUC ROC Curve (Train Set)')\n",
    "\n",
    "    \n",
    "\n",
    "def show_pr_roc_plot(x_train, x_test, y_train, y_test, model):\n",
    "    plt.figure(figsize=(14,6))\n",
    "    \n",
    "    yprob_test = model.predict_proba(x_test)[:,1]\n",
    "\n",
    "    #retrieve the predicted class for each of the test set observations\n",
    "    ypred_test = model.predict(x_test)\n",
    "\n",
    "    precision_test, recall_test, thresholds_test = metrics.precision_recall_curve(y_test, yprob_test)\n",
    "    auc_pr_test = metrics.average_precision_score(y_test,yprob_test)\n",
    "    no_skill = sum(y_test)/len(y_test)\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.plot(recall_test,precision_test, color='green',label='XGBoost (area = %0.2f)' % auc_pr_test)\n",
    "    plt.plot([0, 1], [no_skill, no_skill],linestyle='--',color = 'blue',label='Random (%0.3f)' % no_skill)\n",
    "    plt.legend()\n",
    "    plt.xlabel('Recall')\n",
    "    plt.ylabel('Precision')\n",
    "    plt.title('Precision Recall Curve (Test Set)')\n",
    "\n",
    "    yprob_train = model.predict_proba(x_train)[:,1]\n",
    "\n",
    "    #retrieve the predicted class for each of the test set observations\n",
    "    ypred_train = model.predict(x_train)\n",
    "\n",
    "    precision_train, recall_train, thresholds_train = metrics.precision_recall_curve(y_train, yprob_train)\n",
    "    auc_pr_train = metrics.average_precision_score(y_train,yprob_train)\n",
    "    no_skill = sum(y_train)/len(y_train)\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.plot(recall_train,precision_train, color='green',label='XGBoost (area = %0.2f)' % auc_pr_train)\n",
    "    plt.plot([0, 1], [no_skill, no_skill],linestyle='--',color = 'blue',label='Random (%0.3f)' % no_skill)\n",
    "    plt.legend()\n",
    "    plt.xlabel('Recall')\n",
    "    plt.ylabel('Precision')\n",
    "    plt.title('Precision Recall Curve (Train Set)')\n",
    "\n",
    "def show_gains_chart(x_train, x_test, y_train, y_test, model):\n",
    "    plt.figure(figsize=(14,6))\n",
    "    #Cumulative Gain Plot Test Set\n",
    "    yprob_test = model.predict_proba(x_test)[:,1]\n",
    "\n",
    "    #retrieve the predicted class for each of the test set observations\n",
    "    ypred_test = model.predict(x_test)\n",
    "    fig, ax = plt.subplots()\n",
    "    kds.metrics.plot_cumulative_gain(y_test,yprob_test)\n",
    "\n",
    "    ax.legend().get_texts()[0].set_text('XGBoost')\n",
    "    plt.ylabel('Gain')\n",
    "    plt.title('Cumulative Gain Plot (Test Set)')\n",
    "    #plt.savefig('Gains_Plot_Test.png')\n",
    "\n",
    "    #Cumulative Gain Plot Training Set\n",
    "    yprob_train = model.predict_proba(x_train)[:,1]\n",
    "\n",
    "    #retrieve the predicted class for each of the test set observations\n",
    "    ypred_train = model.predict(x_train)\n",
    "    fig, ax = plt.subplots()\n",
    "    kds.metrics.plot_cumulative_gain(y_train,yprob_train)\n",
    "    ax.legend().get_texts()[0].set_text('XGBoost')\n",
    "    plt.ylabel('Gain')\n",
    "    plt.title('Cumulative Gain Plot (Training Set)')\n",
    "    #plt.savefig('Gains_Plot_Train.png')\n",
    "\n",
    "def show_lift_chart(x_train, x_test, y_train, y_test, model):\n",
    "\n",
    "    plt.figure(figsize=(14,6))\n",
    "    #Lift Plot Test Set\n",
    "    yprob_test = model.predict_proba(x_test)[:,1]\n",
    "\n",
    "    #retrieve the predicted class for each of the test set observations\n",
    "    ypred_test = model.predict(x_test)\n",
    "    fig, ax = plt.subplots()\n",
    "    kds.metrics.plot_lift(y_test,yprob_test)\n",
    "    ax.legend().get_texts()[0].set_text('XGBoost')\n",
    "    plt.title('Lift Plot (Test Set)')\n",
    "    #plt.savefig('Lift_Plot_Test.png')\n",
    "\n",
    "    #Lift Plot Training Set\n",
    "    yprob_train = model.predict_proba(x_train)[:,1]\n",
    "\n",
    "    #retrieve the predicted class for each of the test set observations\n",
    "    ypred_train = model.predict(x_train)\n",
    "    fig, ax = plt.subplots()\n",
    "    kds.metrics.plot_lift(y_train,yprob_train)\n",
    "    ax.legend().get_texts()[0].set_text('XGBoost')\n",
    "    plt.title('Lift Plot (Training Set)')\n",
    "    #plt.savefig('Lift_Plot_Train.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = pd.read_csv(os.getcwd()+\"\\\\data.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>DAYS_REGISTRATION</th>\n",
       "      <th>DAYS_ID_PUBLISH</th>\n",
       "      <th>OWN_CAR_AGE</th>\n",
       "      <th>FLAG_EMP_PHONE</th>\n",
       "      <th>FLAG_WORK_PHONE</th>\n",
       "      <th>FLAG_PHONE</th>\n",
       "      <th>FLAG_EMAIL</th>\n",
       "      <th>REGION_RATING_CLIENT</th>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <th>REG_REGION_NOT_WORK_REGION</th>\n",
       "      <th>REG_CITY_NOT_LIVE_CITY</th>\n",
       "      <th>REG_CITY_NOT_WORK_CITY</th>\n",
       "      <th>LIVE_CITY_NOT_WORK_CITY</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <th>APARTMENTS_AVG</th>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_AVG</th>\n",
       "      <th>YEARS_BUILD_AVG</th>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <th>ELEVATORS_AVG</th>\n",
       "      <th>ENTRANCES_AVG</th>\n",
       "      <th>FLOORSMAX_AVG</th>\n",
       "      <th>FLOORSMIN_AVG</th>\n",
       "      <th>LANDAREA_AVG</th>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "      <th>bureau_DAYS_CREDIT_mean</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_ENDDATE_min</th>\n",
       "      <th>bureau_DAYS_ENDDATE_FACT_max</th>\n",
       "      <th>bureau_AMT_CREDIT_MAX_OVERDUE_max</th>\n",
       "      <th>bureau_CNT_CREDIT_PROLONG_max</th>\n",
       "      <th>bureau_AMT_CREDIT_SUM_min</th>\n",
       "      <th>bureau_AMT_CREDIT_SUM_mean</th>\n",
       "      <th>bureau_AMT_CREDIT_SUM_max</th>\n",
       "      <th>bureau_AMT_CREDIT_SUM_var</th>\n",
       "      <th>bureau_AMT_CREDIT_SUM_DEBT_mean</th>\n",
       "      <th>bureau_AMT_CREDIT_SUM_LIMIT_max</th>\n",
       "      <th>bureau_AMT_CREDIT_SUM_OVERDUE_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\n",
       "      <th>bureau_AMT_ANNUITY_min</th>\n",
       "      <th>bureau_AMT_ANNUITY_mean</th>\n",
       "      <th>bureau_AMT_ANNUITY_max</th>\n",
       "      <th>bureau_CREDIT_TYPE_Car loan_sum</th>\n",
       "      <th>bureau_CREDIT_TYPE_Consumer credit_sum</th>\n",
       "      <th>bureau_CREDIT_TYPE_Credit card_sum</th>\n",
       "      <th>bureau_CREDIT_TYPE_Microloan_sum</th>\n",
       "      <th>bureau_CREDIT_TYPE_Mortgage_sum</th>\n",
       "      <th>credit_card_MONTHS_BALANCE_max</th>\n",
       "      <th>credit_card_MONTHS_BALANCE_mean</th>\n",
       "      <th>credit_card_MONTHS_BALANCE_std</th>\n",
       "      <th>credit_card_AMT_BALANCE_max</th>\n",
       "      <th>credit_card_AMT_BALANCE_sum</th>\n",
       "      <th>credit_card_AMT_CREDIT_LIMIT_ACTUAL_max</th>\n",
       "      <th>credit_card_AMT_CREDIT_LIMIT_ACTUAL_sum</th>\n",
       "      <th>credit_card_AMT_CREDIT_LIMIT_ACTUAL_std</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_ATM_CURRENT_max</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_ATM_CURRENT_mean</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_ATM_CURRENT_sum</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_ATM_CURRENT_median</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_CURRENT_max</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_CURRENT_mean</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_CURRENT_sum</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_CURRENT_median</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_OTHER_CURRENT_max</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_OTHER_CURRENT_mean</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_OTHER_CURRENT_median</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_POS_CURRENT_max</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_POS_CURRENT_mean</th>\n",
       "      <th>credit_card_AMT_DRAWINGS_POS_CURRENT_sum</th>\n",
       "      <th>credit_card_AMT_PAYMENT_CURRENT_max</th>\n",
       "      <th>credit_card_AMT_PAYMENT_CURRENT_median</th>\n",
       "      <th>credit_card_CNT_DRAWINGS_ATM_CURRENT_max</th>\n",
       "      <th>credit_card_CNT_DRAWINGS_ATM_CURRENT_mean</th>\n",
       "      <th>credit_card_CNT_DRAWINGS_ATM_CURRENT_sum</th>\n",
       "      <th>credit_card_CNT_DRAWINGS_ATM_CURRENT_median</th>\n",
       "      <th>credit_card_CNT_DRAWINGS_CURRENT_max</th>\n",
       "      <th>credit_card_CNT_DRAWINGS_CURRENT_mean</th>\n",
       "      <th>credit_card_CNT_DRAWINGS_CURRENT_sum</th>\n",
       "      <th>credit_card_CNT_DRAWINGS_OTHER_CURRENT_max</th>\n",
       "      <th>credit_card_CNT_DRAWINGS_OTHER_CURRENT_mean</th>\n",
       "      <th>credit_card_CNT_DRAWINGS_OTHER_CURRENT_sum</th>\n",
       "      <th>credit_card_CNT_INSTALMENT_MATURE_CUM_max</th>\n",
       "      <th>credit_card_SK_DPD_max</th>\n",
       "      <th>credit_card_SK_DPD_DEF_max</th>\n",
       "      <th>credit_card_PNT_BALANCE_DRAWINGS_ATM_max</th>\n",
       "      <th>credit_card_PNT_BALANCE_DRAWINGS_ATM_mean</th>\n",
       "      <th>credit_card_PNT_BALANCE_DRAWINGS_ATM_median</th>\n",
       "      <th>credit_card_PNT_BALANCE_DRAWINGS_ATM_std</th>\n",
       "      <th>credit_card_AVG_DRAWINGS_ATM_CURRENT_max</th>\n",
       "      <th>credit_card_AVG_DRAWINGS_ATM_CURRENT_mean</th>\n",
       "      <th>credit_card_AVG_DRAWINGS_POS_CURRENT_max</th>\n",
       "      <th>credit_card_AVG_DRAWINGS_POS_CURRENT_mean</th>\n",
       "      <th>credit_card_AVG_DRAWINGS_POS_CURRENT_sum</th>\n",
       "      <th>cash_balance_CNT_INSTALMENT_min</th>\n",
       "      <th>cash_balance_CNT_INSTALMENT_mean</th>\n",
       "      <th>cash_balance_CNT_INSTALMENT_max</th>\n",
       "      <th>cash_balance_CNT_INSTALMENT_FUTURE_min</th>\n",
       "      <th>cash_balance_SK_DPD_min</th>\n",
       "      <th>cash_balance_SK_DPD_mean</th>\n",
       "      <th>cash_balance_SK_DPD_DEF_mean</th>\n",
       "      <th>prev_app_AMT_ANNUITY_max</th>\n",
       "      <th>prev_app_AMT_ANNUITY_mean</th>\n",
       "      <th>prev_app_AMT_ANNUITY_min</th>\n",
       "      <th>prev_app_AMT_APPLICATION_max</th>\n",
       "      <th>prev_app_AMT_APPLICATION_mean</th>\n",
       "      <th>prev_app_AMT_APPLICATION_min</th>\n",
       "      <th>prev_app_AMT_DOWN_PAYMENT_max</th>\n",
       "      <th>prev_app_AMT_DOWN_PAYMENT_min</th>\n",
       "      <th>prev_app_AMT_GOODS_PRICE_min</th>\n",
       "      <th>prev_app_RATE_DOWN_PAYMENT_max</th>\n",
       "      <th>prev_app_RATE_DOWN_PAYMENT_mean</th>\n",
       "      <th>prev_app_RATE_DOWN_PAYMENT_min</th>\n",
       "      <th>prev_app_DAYS_DECISION_max</th>\n",
       "      <th>prev_app_DAYS_DECISION_mean</th>\n",
       "      <th>prev_app_DAYS_DECISION_min</th>\n",
       "      <th>prev_app_DAYS_DECISION_std</th>\n",
       "      <th>prev_app_CNT_PAYMENT_max</th>\n",
       "      <th>prev_app_CNT_PAYMENT_mean</th>\n",
       "      <th>prev_app_CNT_PAYMENT_min</th>\n",
       "      <th>prev_app_DAYS_FIRST_DUE_max</th>\n",
       "      <th>prev_app_DAYS_LAST_DUE_1ST_VERSION_max</th>\n",
       "      <th>prev_app_DAYS_LAST_DUE_1ST_VERSION_std</th>\n",
       "      <th>prev_app_DAYS_LAST_DUE_max</th>\n",
       "      <th>prev_app_DAYS_LAST_DUE_std</th>\n",
       "      <th>prev_app_DOWN_PAYMENT_PNT_var</th>\n",
       "      <th>prev_app_ANNUITY_CREDIT_RATIO_max</th>\n",
       "      <th>prev_app_ANNUITY_CREDIT_RATIO_mean</th>\n",
       "      <th>prev_app_ANNUITY_CREDIT_RATIO_min</th>\n",
       "      <th>prev_app_ANNUITY_CREDIT_RATIO_std</th>\n",
       "      <th>prev_app_NAME_CASH_LOAN_PURPOSE_Building a house or an annex_sum</th>\n",
       "      <th>prev_app_NAME_CASH_LOAN_PURPOSE_Buying a new car_sum</th>\n",
       "      <th>prev_app_NAME_CASH_LOAN_PURPOSE_Buying a used car_sum</th>\n",
       "      <th>prev_app_NAME_CASH_LOAN_PURPOSE_Medicine_sum</th>\n",
       "      <th>prev_app_NAME_CASH_LOAN_PURPOSE_Other_sum</th>\n",
       "      <th>prev_app_NAME_CASH_LOAN_PURPOSE_Repairs_sum</th>\n",
       "      <th>prev_app_NAME_CASH_LOAN_PURPOSE_Urgent needs_sum</th>\n",
       "      <th>prev_app_NAME_CASH_LOAN_PURPOSE_XAP_sum</th>\n",
       "      <th>prev_app_NAME_PAYMENT_TYPE_Cash through the bank_sum</th>\n",
       "      <th>prev_app_NAME_PAYMENT_TYPE_Non-cash from your account_sum</th>\n",
       "      <th>prev_app_WEEKDAY_APPR_PROCESS_START_FRIDAY_sum</th>\n",
       "      <th>prev_app_WEEKDAY_APPR_PROCESS_START_MONDAY_sum</th>\n",
       "      <th>prev_app_WEEKDAY_APPR_PROCESS_START_SATURDAY_sum</th>\n",
       "      <th>prev_app_WEEKDAY_APPR_PROCESS_START_SUNDAY_sum</th>\n",
       "      <th>prev_app_WEEKDAY_APPR_PROCESS_START_THURSDAY_sum</th>\n",
       "      <th>prev_app_WEEKDAY_APPR_PROCESS_START_TUESDAY_sum</th>\n",
       "      <th>prev_app_WEEKDAY_APPR_PROCESS_START_WEDNESDAY_sum</th>\n",
       "      <th>prev_app_CODE_REJECT_REASON_CLIENT_sum</th>\n",
       "      <th>prev_app_CODE_REJECT_REASON_HC_sum</th>\n",
       "      <th>prev_app_CODE_REJECT_REASON_LIMIT_sum</th>\n",
       "      <th>prev_app_CODE_REJECT_REASON_SCO_sum</th>\n",
       "      <th>prev_app_CODE_REJECT_REASON_SCOFR_sum</th>\n",
       "      <th>prev_app_CODE_REJECT_REASON_SYSTEM_sum</th>\n",
       "      <th>prev_app_CODE_REJECT_REASON_XAP_sum</th>\n",
       "      <th>prev_app_NAME_CLIENT_TYPE_New_sum</th>\n",
       "      <th>prev_app_NAME_CLIENT_TYPE_Refreshed_sum</th>\n",
       "      <th>prev_app_NAME_GOODS_CATEGORY_Audio/Video_sum</th>\n",
       "      <th>prev_app_NAME_GOODS_CATEGORY_Auto Accessories_sum</th>\n",
       "      <th>prev_app_NAME_GOODS_CATEGORY_Clothing and Accessories_sum</th>\n",
       "      <th>prev_app_NAME_GOODS_CATEGORY_Computers_sum</th>\n",
       "      <th>prev_app_NAME_GOODS_CATEGORY_Construction Materials_sum</th>\n",
       "      <th>prev_app_NAME_GOODS_CATEGORY_Consumer Electronics_sum</th>\n",
       "      <th>prev_app_NAME_GOODS_CATEGORY_Furniture_sum</th>\n",
       "      <th>prev_app_NAME_GOODS_CATEGORY_Homewares_sum</th>\n",
       "      <th>prev_app_NAME_GOODS_CATEGORY_Mobile_sum</th>\n",
       "      <th>prev_app_NAME_GOODS_CATEGORY_Photo / Cinema Equipment_sum</th>\n",
       "      <th>prev_app_NAME_GOODS_CATEGORY_Tourism_sum</th>\n",
       "      <th>prev_app_NAME_PORTFOLIO_Cards_sum</th>\n",
       "      <th>prev_app_NAME_PORTFOLIO_Cash_sum</th>\n",
       "      <th>prev_app_NAME_PRODUCT_TYPE_walk-in_sum</th>\n",
       "      <th>prev_app_CHANNEL_TYPE_AP+ (Cash loan)_sum</th>\n",
       "      <th>prev_app_CHANNEL_TYPE_Channel of corporate sales_sum</th>\n",
       "      <th>prev_app_CHANNEL_TYPE_Contact center_sum</th>\n",
       "      <th>prev_app_CHANNEL_TYPE_Country-wide_sum</th>\n",
       "      <th>prev_app_CHANNEL_TYPE_Credit and cash offices_sum</th>\n",
       "      <th>prev_app_CHANNEL_TYPE_Regional / Local_sum</th>\n",
       "      <th>prev_app_CHANNEL_TYPE_Stone_sum</th>\n",
       "      <th>prev_app_SELLERPLACE_AREA_sum</th>\n",
       "      <th>prev_app_NAME_SELLER_INDUSTRY_Connectivity_sum</th>\n",
       "      <th>prev_app_NAME_SELLER_INDUSTRY_Construction_sum</th>\n",
       "      <th>prev_app_NAME_SELLER_INDUSTRY_Consumer electronics_sum</th>\n",
       "      <th>prev_app_NAME_SELLER_INDUSTRY_Industry_sum</th>\n",
       "      <th>prev_app_NAME_SELLER_INDUSTRY_MLM partners_sum</th>\n",
       "      <th>prev_app_NAME_YIELD_GROUP_high_sum</th>\n",
       "      <th>prev_app_NAME_YIELD_GROUP_low_action_sum</th>\n",
       "      <th>prev_app_NAME_YIELD_GROUP_low_normal_sum</th>\n",
       "      <th>prev_app_NAME_YIELD_GROUP_middle_sum</th>\n",
       "      <th>install_payments_AMT_INSTALMENT_mean</th>\n",
       "      <th>NAME_CONTRACT_TYPE_Cash loans</th>\n",
       "      <th>NAME_CONTRACT_TYPE_Revolving loans</th>\n",
       "      <th>CODE_GENDER_F</th>\n",
       "      <th>CODE_GENDER_M</th>\n",
       "      <th>FLAG_OWN_CAR_N</th>\n",
       "      <th>FLAG_OWN_CAR_Y</th>\n",
       "      <th>FLAG_OWN_REALTY_N</th>\n",
       "      <th>FLAG_OWN_REALTY_Y</th>\n",
       "      <th>NAME_TYPE_SUITE_Children</th>\n",
       "      <th>NAME_TYPE_SUITE_Family</th>\n",
       "      <th>NAME_TYPE_SUITE_Group of people</th>\n",
       "      <th>NAME_TYPE_SUITE_Other_A</th>\n",
       "      <th>NAME_TYPE_SUITE_Other_B</th>\n",
       "      <th>NAME_TYPE_SUITE_Spouse, partner</th>\n",
       "      <th>NAME_TYPE_SUITE_Unaccompanied</th>\n",
       "      <th>NAME_INCOME_TYPE_Businessman</th>\n",
       "      <th>NAME_INCOME_TYPE_Commercial associate</th>\n",
       "      <th>NAME_INCOME_TYPE_Maternity leave</th>\n",
       "      <th>NAME_INCOME_TYPE_State servant</th>\n",
       "      <th>NAME_INCOME_TYPE_Student</th>\n",
       "      <th>NAME_INCOME_TYPE_Unemployed</th>\n",
       "      <th>NAME_INCOME_TYPE_Working</th>\n",
       "      <th>NAME_EDUCATION_TYPE_Academic degree</th>\n",
       "      <th>NAME_EDUCATION_TYPE_Higher education</th>\n",
       "      <th>NAME_EDUCATION_TYPE_Incomplete higher</th>\n",
       "      <th>NAME_EDUCATION_TYPE_Lower secondary</th>\n",
       "      <th>NAME_EDUCATION_TYPE_Secondary / secondary special</th>\n",
       "      <th>NAME_FAMILY_STATUS_Civil marriage</th>\n",
       "      <th>NAME_FAMILY_STATUS_Married</th>\n",
       "      <th>NAME_FAMILY_STATUS_Separated</th>\n",
       "      <th>NAME_FAMILY_STATUS_Single / not married</th>\n",
       "      <th>NAME_FAMILY_STATUS_Widow</th>\n",
       "      <th>NAME_HOUSING_TYPE_Co-op apartment</th>\n",
       "      <th>NAME_HOUSING_TYPE_House / apartment</th>\n",
       "      <th>NAME_HOUSING_TYPE_Municipal apartment</th>\n",
       "      <th>NAME_HOUSING_TYPE_Office apartment</th>\n",
       "      <th>NAME_HOUSING_TYPE_Rented apartment</th>\n",
       "      <th>NAME_HOUSING_TYPE_With parents</th>\n",
       "      <th>OCCUPATION_TYPE_Accountants</th>\n",
       "      <th>OCCUPATION_TYPE_Cleaning staff</th>\n",
       "      <th>OCCUPATION_TYPE_Cooking staff</th>\n",
       "      <th>OCCUPATION_TYPE_Core staff</th>\n",
       "      <th>OCCUPATION_TYPE_Drivers</th>\n",
       "      <th>OCCUPATION_TYPE_HR staff</th>\n",
       "      <th>OCCUPATION_TYPE_High skill tech staff</th>\n",
       "      <th>OCCUPATION_TYPE_IT staff</th>\n",
       "      <th>OCCUPATION_TYPE_Laborers</th>\n",
       "      <th>OCCUPATION_TYPE_Low-skill Laborers</th>\n",
       "      <th>OCCUPATION_TYPE_Managers</th>\n",
       "      <th>OCCUPATION_TYPE_Medicine staff</th>\n",
       "      <th>OCCUPATION_TYPE_Private service staff</th>\n",
       "      <th>OCCUPATION_TYPE_Realty agents</th>\n",
       "      <th>OCCUPATION_TYPE_Sales staff</th>\n",
       "      <th>OCCUPATION_TYPE_Secretaries</th>\n",
       "      <th>OCCUPATION_TYPE_Security staff</th>\n",
       "      <th>OCCUPATION_TYPE_Waiters/barmen staff</th>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START_FRIDAY</th>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START_MONDAY</th>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START_SATURDAY</th>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START_SUNDAY</th>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START_THURSDAY</th>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START_TUESDAY</th>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START_WEDNESDAY</th>\n",
       "      <th>ORGANIZATION_TYPE_Advertising</th>\n",
       "      <th>ORGANIZATION_TYPE_Agriculture</th>\n",
       "      <th>ORGANIZATION_TYPE_Bank</th>\n",
       "      <th>ORGANIZATION_TYPE_Business Entity Type 1</th>\n",
       "      <th>ORGANIZATION_TYPE_Business Entity Type 2</th>\n",
       "      <th>ORGANIZATION_TYPE_Business Entity Type 3</th>\n",
       "      <th>ORGANIZATION_TYPE_Cleaning</th>\n",
       "      <th>ORGANIZATION_TYPE_Construction</th>\n",
       "      <th>ORGANIZATION_TYPE_Culture</th>\n",
       "      <th>ORGANIZATION_TYPE_Electricity</th>\n",
       "      <th>ORGANIZATION_TYPE_Emergency</th>\n",
       "      <th>ORGANIZATION_TYPE_Government</th>\n",
       "      <th>ORGANIZATION_TYPE_Hotel</th>\n",
       "      <th>ORGANIZATION_TYPE_Housing</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 1</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 10</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 11</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 12</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 13</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 2</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 3</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 4</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 5</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 6</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 7</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 8</th>\n",
       "      <th>ORGANIZATION_TYPE_Industry: type 9</th>\n",
       "      <th>ORGANIZATION_TYPE_Insurance</th>\n",
       "      <th>ORGANIZATION_TYPE_Kindergarten</th>\n",
       "      <th>ORGANIZATION_TYPE_Legal Services</th>\n",
       "      <th>ORGANIZATION_TYPE_Medicine</th>\n",
       "      <th>ORGANIZATION_TYPE_Military</th>\n",
       "      <th>ORGANIZATION_TYPE_Mobile</th>\n",
       "      <th>ORGANIZATION_TYPE_Other</th>\n",
       "      <th>ORGANIZATION_TYPE_Police</th>\n",
       "      <th>ORGANIZATION_TYPE_Postal</th>\n",
       "      <th>ORGANIZATION_TYPE_Realtor</th>\n",
       "      <th>ORGANIZATION_TYPE_Religion</th>\n",
       "      <th>ORGANIZATION_TYPE_Restaurant</th>\n",
       "      <th>ORGANIZATION_TYPE_School</th>\n",
       "      <th>ORGANIZATION_TYPE_Security</th>\n",
       "      <th>ORGANIZATION_TYPE_Security Ministries</th>\n",
       "      <th>ORGANIZATION_TYPE_Self-employed</th>\n",
       "      <th>ORGANIZATION_TYPE_Services</th>\n",
       "      <th>ORGANIZATION_TYPE_Telecom</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 1</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 2</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 3</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 4</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 5</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 6</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 7</th>\n",
       "      <th>ORGANIZATION_TYPE_Transport: type 1</th>\n",
       "      <th>ORGANIZATION_TYPE_Transport: type 2</th>\n",
       "      <th>ORGANIZATION_TYPE_Transport: type 3</th>\n",
       "      <th>ORGANIZATION_TYPE_Transport: type 4</th>\n",
       "      <th>ORGANIZATION_TYPE_University</th>\n",
       "      <th>FONDKAPREMONT_MODE_not specified</th>\n",
       "      <th>FONDKAPREMONT_MODE_org spec account</th>\n",
       "      <th>FONDKAPREMONT_MODE_reg oper account</th>\n",
       "      <th>FONDKAPREMONT_MODE_reg oper spec account</th>\n",
       "      <th>WALLSMATERIAL_MODE_Block</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",
       "    </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>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>0.018801</td>\n",
       "      <td>-9461</td>\n",
       "      <td>-637</td>\n",
       "      <td>-3648.0</td>\n",
       "      <td>-2120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.083037</td>\n",
       "      <td>0.262949</td>\n",
       "      <td>0.139376</td>\n",
       "      <td>0.0247</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.9722</td>\n",
       "      <td>0.6192</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0690</td>\n",
       "      <td>0.0833</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1134.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-874.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1072.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>5043.645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108131.945625</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>2.133807e+10</td>\n",
       "      <td>49156.2</td>\n",
       "      <td>31988.565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>24.0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9251.775</td>\n",
       "      <td>9251.775</td>\n",
       "      <td>9251.775</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>179055.00</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
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       "      <td>-225</td>\n",
       "      <td>-4260.0</td>\n",
       "      <td>-2531</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62947.088438</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <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",
       "      <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</td>\n",
       "      <td>1</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</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</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</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</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</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</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</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>0.028663</td>\n",
       "      <td>-19932</td>\n",
       "      <td>-3038</td>\n",
       "      <td>-4311.0</td>\n",
       "      <td>-3458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.322738</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1106.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1149.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-783.0</td>\n",
       "      <td>-783.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146250.0</td>\n",
       "      <td>146250.000000</td>\n",
       "      <td>146250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-783.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>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",
       "      <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",
       "      <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",
       "      <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",
       "      <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>10.0</td>\n",
       "      <td>15.333333</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22678.785</td>\n",
       "      <td>12278.805</td>\n",
       "      <td>1834.290</td>\n",
       "      <td>247500.0</td>\n",
       "      <td>150530.25</td>\n",
       "      <td>17176.5</td>\n",
       "      <td>3676.5</td>\n",
       "      <td>3105.00</td>\n",
       "      <td>17176.50</td>\n",
       "      <td>0.218890</td>\n",
       "      <td>0.159516</td>\n",
       "      <td>0.100143</td>\n",
       "      <td>-374.0</td>\n",
       "      <td>-1222.833333</td>\n",
       "      <td>-2357.0</td>\n",
       "      <td>717.268546</td>\n",
       "      <td>48.0</td>\n",
       "      <td>20.666667</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-344.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>932.826458</td>\n",
       "      <td>-354.0</td>\n",
       "      <td>748.964396</td>\n",
       "      <td>0.011292</td>\n",
       "      <td>0.125499</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>0.045749</td>\n",
       "      <td>0.032229</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>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2455.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12666.444545</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</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>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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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>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</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
       "0      100002       1             0          202500.0    406597.5   \n",
       "1      100003       0             0          270000.0   1293502.5   \n",
       "2      100004       0             0           67500.0    135000.0   \n",
       "3      100006       0             0          135000.0    312682.5   \n",
       "4      100007       0             0          121500.0    513000.0   \n",
       "\n",
       "   AMT_ANNUITY  REGION_POPULATION_RELATIVE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
       "0      24700.5                    0.018801       -9461           -637   \n",
       "1      35698.5                    0.003541      -16765          -1188   \n",
       "2       6750.0                    0.010032      -19046           -225   \n",
       "3      29686.5                    0.008019      -19005          -3039   \n",
       "4      21865.5                    0.028663      -19932          -3038   \n",
       "\n",
       "   DAYS_REGISTRATION  DAYS_ID_PUBLISH  OWN_CAR_AGE  FLAG_EMP_PHONE  \\\n",
       "0            -3648.0            -2120          NaN               1   \n",
       "1            -1186.0             -291          NaN               1   \n",
       "2            -4260.0            -2531         26.0               1   \n",
       "3            -9833.0            -2437          NaN               1   \n",
       "4            -4311.0            -3458          NaN               1   \n",
       "\n",
       "   FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL  REGION_RATING_CLIENT  \\\n",
       "0                0           1           0                     2   \n",
       "1                0           1           0                     1   \n",
       "2                1           1           0                     2   \n",
       "3                0           0           0                     2   \n",
       "4                0           0           0                     2   \n",
       "\n",
       "   HOUR_APPR_PROCESS_START  REG_REGION_NOT_WORK_REGION  \\\n",
       "0                       10                           0   \n",
       "1                       11                           0   \n",
       "2                        9                           0   \n",
       "3                       17                           0   \n",
       "4                       11                           0   \n",
       "\n",
       "   REG_CITY_NOT_LIVE_CITY  REG_CITY_NOT_WORK_CITY  LIVE_CITY_NOT_WORK_CITY  \\\n",
       "0                       0                       0                        0   \n",
       "1                       0                       0                        0   \n",
       "2                       0                       0                        0   \n",
       "3                       0                       0                        0   \n",
       "4                       0                       1                        1   \n",
       "\n",
       "   EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  APARTMENTS_AVG  BASEMENTAREA_AVG  \\\n",
       "0      0.083037      0.262949      0.139376          0.0247            0.0369   \n",
       "1      0.311267      0.622246           NaN          0.0959            0.0529   \n",
       "2           NaN      0.555912      0.729567             NaN               NaN   \n",
       "3           NaN      0.650442           NaN             NaN               NaN   \n",
       "4           NaN      0.322738           NaN             NaN               NaN   \n",
       "\n",
       "   YEARS_BEGINEXPLUATATION_AVG  YEARS_BUILD_AVG  COMMONAREA_AVG  \\\n",
       "0                       0.9722           0.6192          0.0143   \n",
       "1                       0.9851           0.7960          0.0605   \n",
       "2                          NaN              NaN             NaN   \n",
       "3                          NaN              NaN             NaN   \n",
       "4                          NaN              NaN             NaN   \n",
       "\n",
       "   ELEVATORS_AVG  ENTRANCES_AVG  FLOORSMAX_AVG  FLOORSMIN_AVG  LANDAREA_AVG  \\\n",
       "0           0.00         0.0690         0.0833         0.1250        0.0369   \n",
       "1           0.08         0.0345         0.2917         0.3333        0.0130   \n",
       "2            NaN            NaN            NaN            NaN           NaN   \n",
       "3            NaN            NaN            NaN            NaN           NaN   \n",
       "4            NaN            NaN            NaN            NaN           NaN   \n",
       "\n",
       "   NONLIVINGAPARTMENTS_AVG  NONLIVINGAREA_AVG  OBS_30_CNT_SOCIAL_CIRCLE  \\\n",
       "0                   0.0000             0.0000                       2.0   \n",
       "1                   0.0039             0.0098                       1.0   \n",
       "2                      NaN                NaN                       0.0   \n",
       "3                      NaN                NaN                       2.0   \n",
       "4                      NaN                NaN                       0.0   \n",
       "\n",
       "   DEF_30_CNT_SOCIAL_CIRCLE  DAYS_LAST_PHONE_CHANGE  \\\n",
       "0                       2.0                 -1134.0   \n",
       "1                       0.0                  -828.0   \n",
       "2                       0.0                  -815.0   \n",
       "3                       0.0                  -617.0   \n",
       "4                       0.0                 -1106.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "3                        NaN                        NaN   \n",
       "4                        0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_YEAR  bureau_DAYS_CREDIT_mean  \\\n",
       "0                         1.0                  -874.00   \n",
       "1                         0.0                 -1400.75   \n",
       "2                         0.0                  -867.00   \n",
       "3                         NaN                      NaN   \n",
       "4                         0.0                 -1149.00   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_mean  bureau_DAYS_CREDIT_ENDDATE_min  \\\n",
       "0                             0.0                         -1072.0   \n",
       "1                             0.0                         -2434.0   \n",
       "2                             0.0                          -595.0   \n",
       "3                             NaN                             NaN   \n",
       "4                             0.0                          -783.0   \n",
       "\n",
       "   bureau_DAYS_ENDDATE_FACT_max  bureau_AMT_CREDIT_MAX_OVERDUE_max  \\\n",
       "0                         -36.0                           5043.645   \n",
       "1                        -540.0                              0.000   \n",
       "2                        -382.0                              0.000   \n",
       "3                           NaN                                NaN   \n",
       "4                        -783.0                              0.000   \n",
       "\n",
       "   bureau_CNT_CREDIT_PROLONG_max  bureau_AMT_CREDIT_SUM_min  \\\n",
       "0                            0.0                        0.0   \n",
       "1                            0.0                    22248.0   \n",
       "2                            0.0                    94500.0   \n",
       "3                            NaN                        NaN   \n",
       "4                            0.0                   146250.0   \n",
       "\n",
       "   bureau_AMT_CREDIT_SUM_mean  bureau_AMT_CREDIT_SUM_max  \\\n",
       "0               108131.945625                   450000.0   \n",
       "1               254350.125000                   810000.0   \n",
       "2                94518.900000                    94537.8   \n",
       "3                         NaN                        NaN   \n",
       "4               146250.000000                   146250.0   \n",
       "\n",
       "   bureau_AMT_CREDIT_SUM_var  bureau_AMT_CREDIT_SUM_DEBT_mean  \\\n",
       "0               2.133807e+10                          49156.2   \n",
       "1               1.385846e+11                              0.0   \n",
       "2               7.144200e+02                              0.0   \n",
       "3                        NaN                              NaN   \n",
       "4                        NaN                              0.0   \n",
       "\n",
       "   bureau_AMT_CREDIT_SUM_LIMIT_max  bureau_AMT_CREDIT_SUM_OVERDUE_max  \\\n",
       "0                        31988.565                                0.0   \n",
       "1                       810000.000                                0.0   \n",
       "2                            0.000                                0.0   \n",
       "3                              NaN                                NaN   \n",
       "4                            0.000                                0.0   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_max  bureau_AMT_ANNUITY_min  \\\n",
       "0                           -7.0                     0.0   \n",
       "1                          -43.0                     NaN   \n",
       "2                         -382.0                     NaN   \n",
       "3                            NaN                     NaN   \n",
       "4                         -783.0                     NaN   \n",
       "\n",
       "   bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  \\\n",
       "0                      0.0                     0.0   \n",
       "1                      NaN                     NaN   \n",
       "2                      NaN                     NaN   \n",
       "3                      NaN                     NaN   \n",
       "4                      NaN                     NaN   \n",
       "\n",
       "   bureau_CREDIT_TYPE_Car loan_sum  bureau_CREDIT_TYPE_Consumer credit_sum  \\\n",
       "0                              0.0                                     4.0   \n",
       "1                              0.0                                     2.0   \n",
       "2                              0.0                                     2.0   \n",
       "3                              NaN                                     NaN   \n",
       "4                              0.0                                     1.0   \n",
       "\n",
       "   bureau_CREDIT_TYPE_Credit card_sum  bureau_CREDIT_TYPE_Microloan_sum  \\\n",
       "0                                 4.0                               0.0   \n",
       "1                                 2.0                               0.0   \n",
       "2                                 0.0                               0.0   \n",
       "3                                 NaN                               NaN   \n",
       "4                                 0.0                               0.0   \n",
       "\n",
       "   bureau_CREDIT_TYPE_Mortgage_sum  credit_card_MONTHS_BALANCE_max  \\\n",
       "0                              0.0                             NaN   \n",
       "1                              0.0                             NaN   \n",
       "2                              0.0                             NaN   \n",
       "3                              NaN                            -1.0   \n",
       "4                              0.0                             NaN   \n",
       "\n",
       "   credit_card_MONTHS_BALANCE_mean  credit_card_MONTHS_BALANCE_std  \\\n",
       "0                              NaN                             NaN   \n",
       "1                              NaN                             NaN   \n",
       "2                              NaN                             NaN   \n",
       "3                             -3.5                        1.870829   \n",
       "4                              NaN                             NaN   \n",
       "\n",
       "   credit_card_AMT_BALANCE_max  credit_card_AMT_BALANCE_sum  \\\n",
       "0                          NaN                          NaN   \n",
       "1                          NaN                          NaN   \n",
       "2                          NaN                          NaN   \n",
       "3                          0.0                          0.0   \n",
       "4                          NaN                          NaN   \n",
       "\n",
       "   credit_card_AMT_CREDIT_LIMIT_ACTUAL_max  \\\n",
       "0                                      NaN   \n",
       "1                                      NaN   \n",
       "2                                      NaN   \n",
       "3                                 270000.0   \n",
       "4                                      NaN   \n",
       "\n",
       "   credit_card_AMT_CREDIT_LIMIT_ACTUAL_sum  \\\n",
       "0                                      NaN   \n",
       "1                                      NaN   \n",
       "2                                      NaN   \n",
       "3                                1620000.0   \n",
       "4                                      NaN   \n",
       "\n",
       "   credit_card_AMT_CREDIT_LIMIT_ACTUAL_std  \\\n",
       "0                                      NaN   \n",
       "1                                      NaN   \n",
       "2                                      NaN   \n",
       "3                                      0.0   \n",
       "4                                      NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_ATM_CURRENT_max  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       NaN   \n",
       "4                                       NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_ATM_CURRENT_mean  \\\n",
       "0                                        NaN   \n",
       "1                                        NaN   \n",
       "2                                        NaN   \n",
       "3                                        NaN   \n",
       "4                                        NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_ATM_CURRENT_sum  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       0.0   \n",
       "4                                       NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_ATM_CURRENT_median  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3                                          NaN   \n",
       "4                                          NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_CURRENT_max  \\\n",
       "0                                   NaN   \n",
       "1                                   NaN   \n",
       "2                                   NaN   \n",
       "3                                   0.0   \n",
       "4                                   NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_CURRENT_mean  \\\n",
       "0                                    NaN   \n",
       "1                                    NaN   \n",
       "2                                    NaN   \n",
       "3                                    0.0   \n",
       "4                                    NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_CURRENT_sum  \\\n",
       "0                                   NaN   \n",
       "1                                   NaN   \n",
       "2                                   NaN   \n",
       "3                                   0.0   \n",
       "4                                   NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_CURRENT_median  \\\n",
       "0                                      NaN   \n",
       "1                                      NaN   \n",
       "2                                      NaN   \n",
       "3                                      0.0   \n",
       "4                                      NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_OTHER_CURRENT_max  \\\n",
       "0                                         NaN   \n",
       "1                                         NaN   \n",
       "2                                         NaN   \n",
       "3                                         NaN   \n",
       "4                                         NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_OTHER_CURRENT_mean  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3                                          NaN   \n",
       "4                                          NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_OTHER_CURRENT_median  \\\n",
       "0                                            NaN   \n",
       "1                                            NaN   \n",
       "2                                            NaN   \n",
       "3                                            NaN   \n",
       "4                                            NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_POS_CURRENT_max  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       NaN   \n",
       "4                                       NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_POS_CURRENT_mean  \\\n",
       "0                                        NaN   \n",
       "1                                        NaN   \n",
       "2                                        NaN   \n",
       "3                                        NaN   \n",
       "4                                        NaN   \n",
       "\n",
       "   credit_card_AMT_DRAWINGS_POS_CURRENT_sum  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       0.0   \n",
       "4                                       NaN   \n",
       "\n",
       "   credit_card_AMT_PAYMENT_CURRENT_max  \\\n",
       "0                                  NaN   \n",
       "1                                  NaN   \n",
       "2                                  NaN   \n",
       "3                                  NaN   \n",
       "4                                  NaN   \n",
       "\n",
       "   credit_card_AMT_PAYMENT_CURRENT_median  \\\n",
       "0                                     NaN   \n",
       "1                                     NaN   \n",
       "2                                     NaN   \n",
       "3                                     NaN   \n",
       "4                                     NaN   \n",
       "\n",
       "   credit_card_CNT_DRAWINGS_ATM_CURRENT_max  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       NaN   \n",
       "4                                       NaN   \n",
       "\n",
       "   credit_card_CNT_DRAWINGS_ATM_CURRENT_mean  \\\n",
       "0                                        NaN   \n",
       "1                                        NaN   \n",
       "2                                        NaN   \n",
       "3                                        NaN   \n",
       "4                                        NaN   \n",
       "\n",
       "   credit_card_CNT_DRAWINGS_ATM_CURRENT_sum  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       0.0   \n",
       "4                                       NaN   \n",
       "\n",
       "   credit_card_CNT_DRAWINGS_ATM_CURRENT_median  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3                                          NaN   \n",
       "4                                          NaN   \n",
       "\n",
       "   credit_card_CNT_DRAWINGS_CURRENT_max  \\\n",
       "0                                   NaN   \n",
       "1                                   NaN   \n",
       "2                                   NaN   \n",
       "3                                   0.0   \n",
       "4                                   NaN   \n",
       "\n",
       "   credit_card_CNT_DRAWINGS_CURRENT_mean  \\\n",
       "0                                    NaN   \n",
       "1                                    NaN   \n",
       "2                                    NaN   \n",
       "3                                    0.0   \n",
       "4                                    NaN   \n",
       "\n",
       "   credit_card_CNT_DRAWINGS_CURRENT_sum  \\\n",
       "0                                   NaN   \n",
       "1                                   NaN   \n",
       "2                                   NaN   \n",
       "3                                   0.0   \n",
       "4                                   NaN   \n",
       "\n",
       "   credit_card_CNT_DRAWINGS_OTHER_CURRENT_max  \\\n",
       "0                                         NaN   \n",
       "1                                         NaN   \n",
       "2                                         NaN   \n",
       "3                                         NaN   \n",
       "4                                         NaN   \n",
       "\n",
       "   credit_card_CNT_DRAWINGS_OTHER_CURRENT_mean  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3                                          NaN   \n",
       "4                                          NaN   \n",
       "\n",
       "   credit_card_CNT_DRAWINGS_OTHER_CURRENT_sum  \\\n",
       "0                                         NaN   \n",
       "1                                         NaN   \n",
       "2                                         NaN   \n",
       "3                                         0.0   \n",
       "4                                         NaN   \n",
       "\n",
       "   credit_card_CNT_INSTALMENT_MATURE_CUM_max  credit_card_SK_DPD_max  \\\n",
       "0                                        NaN                     NaN   \n",
       "1                                        NaN                     NaN   \n",
       "2                                        NaN                     NaN   \n",
       "3                                        0.0                     0.0   \n",
       "4                                        NaN                     NaN   \n",
       "\n",
       "   credit_card_SK_DPD_DEF_max  credit_card_PNT_BALANCE_DRAWINGS_ATM_max  \\\n",
       "0                         NaN                                       NaN   \n",
       "1                         NaN                                       NaN   \n",
       "2                         NaN                                       NaN   \n",
       "3                         0.0                                       NaN   \n",
       "4                         NaN                                       NaN   \n",
       "\n",
       "   credit_card_PNT_BALANCE_DRAWINGS_ATM_mean  \\\n",
       "0                                        NaN   \n",
       "1                                        NaN   \n",
       "2                                        NaN   \n",
       "3                                        NaN   \n",
       "4                                        NaN   \n",
       "\n",
       "   credit_card_PNT_BALANCE_DRAWINGS_ATM_median  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3                                          NaN   \n",
       "4                                          NaN   \n",
       "\n",
       "   credit_card_PNT_BALANCE_DRAWINGS_ATM_std  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       NaN   \n",
       "4                                       NaN   \n",
       "\n",
       "   credit_card_AVG_DRAWINGS_ATM_CURRENT_max  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       NaN   \n",
       "4                                       NaN   \n",
       "\n",
       "   credit_card_AVG_DRAWINGS_ATM_CURRENT_mean  \\\n",
       "0                                        NaN   \n",
       "1                                        NaN   \n",
       "2                                        NaN   \n",
       "3                                        NaN   \n",
       "4                                        NaN   \n",
       "\n",
       "   credit_card_AVG_DRAWINGS_POS_CURRENT_max  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       NaN   \n",
       "4                                       NaN   \n",
       "\n",
       "   credit_card_AVG_DRAWINGS_POS_CURRENT_mean  \\\n",
       "0                                        NaN   \n",
       "1                                        NaN   \n",
       "2                                        NaN   \n",
       "3                                        NaN   \n",
       "4                                        NaN   \n",
       "\n",
       "   credit_card_AVG_DRAWINGS_POS_CURRENT_sum  cash_balance_CNT_INSTALMENT_min  \\\n",
       "0                                       NaN                             24.0   \n",
       "1                                       NaN                              6.0   \n",
       "2                                       NaN                              3.0   \n",
       "3                                       0.0                              1.0   \n",
       "4                                       NaN                             10.0   \n",
       "\n",
       "   cash_balance_CNT_INSTALMENT_mean  cash_balance_CNT_INSTALMENT_max  \\\n",
       "0                         24.000000                             24.0   \n",
       "1                         10.107143                             12.0   \n",
       "2                          3.750000                              4.0   \n",
       "3                         12.000000                             48.0   \n",
       "4                         15.333333                             24.0   \n",
       "\n",
       "   cash_balance_CNT_INSTALMENT_FUTURE_min  cash_balance_SK_DPD_min  \\\n",
       "0                                     6.0                      0.0   \n",
       "1                                     0.0                      0.0   \n",
       "2                                     0.0                      0.0   \n",
       "3                                     0.0                      0.0   \n",
       "4                                     0.0                      0.0   \n",
       "\n",
       "   cash_balance_SK_DPD_mean  cash_balance_SK_DPD_DEF_mean  \\\n",
       "0                       0.0                           0.0   \n",
       "1                       0.0                           0.0   \n",
       "2                       0.0                           0.0   \n",
       "3                       0.0                           0.0   \n",
       "4                       0.0                           0.0   \n",
       "\n",
       "   prev_app_AMT_ANNUITY_max  prev_app_AMT_ANNUITY_mean  \\\n",
       "0                  9251.775                   9251.775   \n",
       "1                 98356.995                  56553.990   \n",
       "2                  5357.250                   5357.250   \n",
       "3                 39954.510                  23651.175   \n",
       "4                 22678.785                  12278.805   \n",
       "\n",
       "   prev_app_AMT_ANNUITY_min  prev_app_AMT_APPLICATION_max  \\\n",
       "0                  9251.775                      179055.0   \n",
       "1                  6737.310                      900000.0   \n",
       "2                  5357.250                       24282.0   \n",
       "3                  2482.920                      688500.0   \n",
       "4                  1834.290                      247500.0   \n",
       "\n",
       "   prev_app_AMT_APPLICATION_mean  prev_app_AMT_APPLICATION_min  \\\n",
       "0                      179055.00                      179055.0   \n",
       "1                      435436.50                       68809.5   \n",
       "2                       24282.00                       24282.0   \n",
       "3                      272203.26                           0.0   \n",
       "4                      150530.25                       17176.5   \n",
       "\n",
       "   prev_app_AMT_DOWN_PAYMENT_max  prev_app_AMT_DOWN_PAYMENT_min  \\\n",
       "0                            0.0                           0.00   \n",
       "1                         6885.0                           0.00   \n",
       "2                         4860.0                        4860.00   \n",
       "3                        66987.0                        2693.34   \n",
       "4                         3676.5                        3105.00   \n",
       "\n",
       "   prev_app_AMT_GOODS_PRICE_min  prev_app_RATE_DOWN_PAYMENT_max  \\\n",
       "0                     179055.00                        0.000000   \n",
       "1                      68809.50                        0.100061   \n",
       "2                      24282.00                        0.212008   \n",
       "3                      26912.34                        0.217830   \n",
       "4                      17176.50                        0.218890   \n",
       "\n",
       "   prev_app_RATE_DOWN_PAYMENT_mean  prev_app_RATE_DOWN_PAYMENT_min  \\\n",
       "0                         0.000000                        0.000000   \n",
       "1                         0.050030                        0.000000   \n",
       "2                         0.212008                        0.212008   \n",
       "3                         0.163412                        0.108994   \n",
       "4                         0.159516                        0.100143   \n",
       "\n",
       "   prev_app_DAYS_DECISION_max  prev_app_DAYS_DECISION_mean  \\\n",
       "0                      -606.0                  -606.000000   \n",
       "1                      -746.0                 -1305.000000   \n",
       "2                      -815.0                  -815.000000   \n",
       "3                      -181.0                  -272.444444   \n",
       "4                      -374.0                 -1222.833333   \n",
       "\n",
       "   prev_app_DAYS_DECISION_min  prev_app_DAYS_DECISION_std  \\\n",
       "0                      -606.0                         NaN   \n",
       "1                     -2341.0                  898.138631   \n",
       "2                      -815.0                         NaN   \n",
       "3                      -617.0                  157.236375   \n",
       "4                     -2357.0                  717.268546   \n",
       "\n",
       "   prev_app_CNT_PAYMENT_max  prev_app_CNT_PAYMENT_mean  \\\n",
       "0                      24.0                  24.000000   \n",
       "1                      12.0                  10.000000   \n",
       "2                       4.0                   4.000000   \n",
       "3                      48.0                  23.000000   \n",
       "4                      48.0                  20.666667   \n",
       "\n",
       "   prev_app_CNT_PAYMENT_min  prev_app_DAYS_FIRST_DUE_max  \\\n",
       "0                      24.0                       -565.0   \n",
       "1                       6.0                       -716.0   \n",
       "2                       4.0                       -784.0   \n",
       "3                       0.0                       -151.0   \n",
       "4                      10.0                       -344.0   \n",
       "\n",
       "   prev_app_DAYS_LAST_DUE_1ST_VERSION_max  \\\n",
       "0                                   125.0   \n",
       "1                                  -386.0   \n",
       "2                                  -694.0   \n",
       "3                                  1259.0   \n",
       "4                                   346.0   \n",
       "\n",
       "   prev_app_DAYS_LAST_DUE_1ST_VERSION_std  prev_app_DAYS_LAST_DUE_max  \\\n",
       "0                                     NaN                       -25.0   \n",
       "1                              854.970370                      -536.0   \n",
       "2                                     NaN                      -724.0   \n",
       "3                              785.967769                      -151.0   \n",
       "4                              932.826458                      -354.0   \n",
       "\n",
       "   prev_app_DAYS_LAST_DUE_std  prev_app_DOWN_PAYMENT_PNT_var  \\\n",
       "0                         NaN                            NaN   \n",
       "1                  803.569744                       0.005118   \n",
       "2                         NaN                            NaN   \n",
       "3                  193.747258                       0.009634   \n",
       "4                  748.964396                       0.011292   \n",
       "\n",
       "   prev_app_ANNUITY_CREDIT_RATIO_max  prev_app_ANNUITY_CREDIT_RATIO_mean  \\\n",
       "0                           0.051670                            0.051670   \n",
       "1                           0.185200                            0.126383   \n",
       "2                           0.266450                            0.266450   \n",
       "3                           0.108340                            0.069304   \n",
       "4                           0.125499                            0.090659   \n",
       "\n",
       "   prev_app_ANNUITY_CREDIT_RATIO_min  prev_app_ANNUITY_CREDIT_RATIO_std  \\\n",
       "0                           0.051670                                NaN   \n",
       "1                           0.094950                           0.050977   \n",
       "2                           0.266450                                NaN   \n",
       "3                           0.035920                           0.032882   \n",
       "4                           0.045749                           0.032229   \n",
       "\n",
       "   prev_app_NAME_CASH_LOAN_PURPOSE_Building a house or an annex_sum  \\\n",
       "0                                                0.0                  \n",
       "1                                                0.0                  \n",
       "2                                                0.0                  \n",
       "3                                                0.0                  \n",
       "4                                                0.0                  \n",
       "\n",
       "   prev_app_NAME_CASH_LOAN_PURPOSE_Buying a new car_sum  \\\n",
       "0                                                0.0      \n",
       "1                                                0.0      \n",
       "2                                                0.0      \n",
       "3                                                0.0      \n",
       "4                                                0.0      \n",
       "\n",
       "   prev_app_NAME_CASH_LOAN_PURPOSE_Buying a used car_sum  \\\n",
       "0                                                0.0       \n",
       "1                                                0.0       \n",
       "2                                                0.0       \n",
       "3                                                0.0       \n",
       "4                                                0.0       \n",
       "\n",
       "   prev_app_NAME_CASH_LOAN_PURPOSE_Medicine_sum  \\\n",
       "0                                           0.0   \n",
       "1                                           0.0   \n",
       "2                                           0.0   \n",
       "3                                           0.0   \n",
       "4                                           0.0   \n",
       "\n",
       "   prev_app_NAME_CASH_LOAN_PURPOSE_Other_sum  \\\n",
       "0                                        0.0   \n",
       "1                                        0.0   \n",
       "2                                        0.0   \n",
       "3                                        0.0   \n",
       "4                                        0.0   \n",
       "\n",
       "   prev_app_NAME_CASH_LOAN_PURPOSE_Repairs_sum  \\\n",
       "0                                          0.0   \n",
       "1                                          0.0   \n",
       "2                                          0.0   \n",
       "3                                          0.0   \n",
       "4                                          0.0   \n",
       "\n",
       "   prev_app_NAME_CASH_LOAN_PURPOSE_Urgent needs_sum  \\\n",
       "0                                               0.0   \n",
       "1                                               0.0   \n",
       "2                                               0.0   \n",
       "3                                               0.0   \n",
       "4                                               0.0   \n",
       "\n",
       "   prev_app_NAME_CASH_LOAN_PURPOSE_XAP_sum  \\\n",
       "0                                      1.0   \n",
       "1                                      2.0   \n",
       "2                                      1.0   \n",
       "3                                      4.0   \n",
       "4                                      2.0   \n",
       "\n",
       "   prev_app_NAME_PAYMENT_TYPE_Cash through the bank_sum  \\\n",
       "0                                                0.0      \n",
       "1                                                2.0      \n",
       "2                                                1.0      \n",
       "3                                                4.0      \n",
       "4                                                5.0      \n",
       "\n",
       "   prev_app_NAME_PAYMENT_TYPE_Non-cash from your account_sum  \\\n",
       "0                                                0.0           \n",
       "1                                                0.0           \n",
       "2                                                0.0           \n",
       "3                                                0.0           \n",
       "4                                                0.0           \n",
       "\n",
       "   prev_app_WEEKDAY_APPR_PROCESS_START_FRIDAY_sum  \\\n",
       "0                                             0.0   \n",
       "1                                             1.0   \n",
       "2                                             1.0   \n",
       "3                                             0.0   \n",
       "4                                             1.0   \n",
       "\n",
       "   prev_app_WEEKDAY_APPR_PROCESS_START_MONDAY_sum  \\\n",
       "0                                             0.0   \n",
       "1                                             0.0   \n",
       "2                                             0.0   \n",
       "3                                             0.0   \n",
       "4                                             1.0   \n",
       "\n",
       "   prev_app_WEEKDAY_APPR_PROCESS_START_SATURDAY_sum  \\\n",
       "0                                               1.0   \n",
       "1                                               1.0   \n",
       "2                                               0.0   \n",
       "3                                               1.0   \n",
       "4                                               1.0   \n",
       "\n",
       "   prev_app_WEEKDAY_APPR_PROCESS_START_SUNDAY_sum  \\\n",
       "0                                             0.0   \n",
       "1                                             1.0   \n",
       "2                                             0.0   \n",
       "3                                             1.0   \n",
       "4                                             2.0   \n",
       "\n",
       "   prev_app_WEEKDAY_APPR_PROCESS_START_THURSDAY_sum  \\\n",
       "0                                               0.0   \n",
       "1                                               0.0   \n",
       "2                                               0.0   \n",
       "3                                               6.0   \n",
       "4                                               1.0   \n",
       "\n",
       "   prev_app_WEEKDAY_APPR_PROCESS_START_TUESDAY_sum  \\\n",
       "0                                              0.0   \n",
       "1                                              0.0   \n",
       "2                                              0.0   \n",
       "3                                              1.0   \n",
       "4                                              0.0   \n",
       "\n",
       "   prev_app_WEEKDAY_APPR_PROCESS_START_WEDNESDAY_sum  \\\n",
       "0                                                0.0   \n",
       "1                                                0.0   \n",
       "2                                                0.0   \n",
       "3                                                0.0   \n",
       "4                                                0.0   \n",
       "\n",
       "   prev_app_CODE_REJECT_REASON_CLIENT_sum  prev_app_CODE_REJECT_REASON_HC_sum  \\\n",
       "0                                     0.0                                 0.0   \n",
       "1                                     0.0                                 0.0   \n",
       "2                                     0.0                                 0.0   \n",
       "3                                     0.0                                 0.0   \n",
       "4                                     0.0                                 0.0   \n",
       "\n",
       "   prev_app_CODE_REJECT_REASON_LIMIT_sum  prev_app_CODE_REJECT_REASON_SCO_sum  \\\n",
       "0                                    0.0                                  0.0   \n",
       "1                                    0.0                                  0.0   \n",
       "2                                    0.0                                  0.0   \n",
       "3                                    1.0                                  0.0   \n",
       "4                                    0.0                                  0.0   \n",
       "\n",
       "   prev_app_CODE_REJECT_REASON_SCOFR_sum  \\\n",
       "0                                    0.0   \n",
       "1                                    0.0   \n",
       "2                                    0.0   \n",
       "3                                    0.0   \n",
       "4                                    0.0   \n",
       "\n",
       "   prev_app_CODE_REJECT_REASON_SYSTEM_sum  \\\n",
       "0                                     0.0   \n",
       "1                                     0.0   \n",
       "2                                     0.0   \n",
       "3                                     0.0   \n",
       "4                                     0.0   \n",
       "\n",
       "   prev_app_CODE_REJECT_REASON_XAP_sum  prev_app_NAME_CLIENT_TYPE_New_sum  \\\n",
       "0                                  1.0                                1.0   \n",
       "1                                  3.0                                0.0   \n",
       "2                                  1.0                                1.0   \n",
       "3                                  8.0                                1.0   \n",
       "4                                  6.0                                1.0   \n",
       "\n",
       "   prev_app_NAME_CLIENT_TYPE_Refreshed_sum  \\\n",
       "0                                      0.0   \n",
       "1                                      2.0   \n",
       "2                                      0.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "\n",
       "   prev_app_NAME_GOODS_CATEGORY_Audio/Video_sum  \\\n",
       "0                                           0.0   \n",
       "1                                           0.0   \n",
       "2                                           0.0   \n",
       "3                                           1.0   \n",
       "4                                           2.0   \n",
       "\n",
       "   prev_app_NAME_GOODS_CATEGORY_Auto Accessories_sum  \\\n",
       "0                                                0.0   \n",
       "1                                                0.0   \n",
       "2                                                0.0   \n",
       "3                                                0.0   \n",
       "4                                                0.0   \n",
       "\n",
       "   prev_app_NAME_GOODS_CATEGORY_Clothing and Accessories_sum  \\\n",
       "0                                                0.0           \n",
       "1                                                0.0           \n",
       "2                                                0.0           \n",
       "3                                                0.0           \n",
       "4                                                0.0           \n",
       "\n",
       "   prev_app_NAME_GOODS_CATEGORY_Computers_sum  \\\n",
       "0                                         0.0   \n",
       "1                                         0.0   \n",
       "2                                         0.0   \n",
       "3                                         0.0   \n",
       "4                                         0.0   \n",
       "\n",
       "   prev_app_NAME_GOODS_CATEGORY_Construction Materials_sum  \\\n",
       "0                                                0.0         \n",
       "1                                                0.0         \n",
       "2                                                0.0         \n",
       "3                                                1.0         \n",
       "4                                                0.0         \n",
       "\n",
       "   prev_app_NAME_GOODS_CATEGORY_Consumer Electronics_sum  \\\n",
       "0                                                0.0       \n",
       "1                                                1.0       \n",
       "2                                                0.0       \n",
       "3                                                0.0       \n",
       "4                                                0.0       \n",
       "\n",
       "   prev_app_NAME_GOODS_CATEGORY_Furniture_sum  \\\n",
       "0                                         0.0   \n",
       "1                                         1.0   \n",
       "2                                         0.0   \n",
       "3                                         0.0   \n",
       "4                                         0.0   \n",
       "\n",
       "   prev_app_NAME_GOODS_CATEGORY_Homewares_sum  \\\n",
       "0                                         0.0   \n",
       "1                                         0.0   \n",
       "2                                         0.0   \n",
       "3                                         0.0   \n",
       "4                                         0.0   \n",
       "\n",
       "   prev_app_NAME_GOODS_CATEGORY_Mobile_sum  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      1.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "\n",
       "   prev_app_NAME_GOODS_CATEGORY_Photo / Cinema Equipment_sum  \\\n",
       "0                                                0.0           \n",
       "1                                                0.0           \n",
       "2                                                0.0           \n",
       "3                                                0.0           \n",
       "4                                                0.0           \n",
       "\n",
       "   prev_app_NAME_GOODS_CATEGORY_Tourism_sum  \\\n",
       "0                                       0.0   \n",
       "1                                       0.0   \n",
       "2                                       0.0   \n",
       "3                                       0.0   \n",
       "4                                       0.0   \n",
       "\n",
       "   prev_app_NAME_PORTFOLIO_Cards_sum  prev_app_NAME_PORTFOLIO_Cash_sum  \\\n",
       "0                                0.0                               0.0   \n",
       "1                                0.0                               1.0   \n",
       "2                                0.0                               0.0   \n",
       "3                                1.0                               3.0   \n",
       "4                                0.0                               4.0   \n",
       "\n",
       "   prev_app_NAME_PRODUCT_TYPE_walk-in_sum  \\\n",
       "0                                     0.0   \n",
       "1                                     0.0   \n",
       "2                                     0.0   \n",
       "3                                     0.0   \n",
       "4                                     1.0   \n",
       "\n",
       "   prev_app_CHANNEL_TYPE_AP+ (Cash loan)_sum  \\\n",
       "0                                        0.0   \n",
       "1                                        0.0   \n",
       "2                                        0.0   \n",
       "3                                        0.0   \n",
       "4                                        1.0   \n",
       "\n",
       "   prev_app_CHANNEL_TYPE_Channel of corporate sales_sum  \\\n",
       "0                                                0.0      \n",
       "1                                                0.0      \n",
       "2                                                0.0      \n",
       "3                                                0.0      \n",
       "4                                                0.0      \n",
       "\n",
       "   prev_app_CHANNEL_TYPE_Contact center_sum  \\\n",
       "0                                       0.0   \n",
       "1                                       0.0   \n",
       "2                                       0.0   \n",
       "3                                       0.0   \n",
       "4                                       0.0   \n",
       "\n",
       "   prev_app_CHANNEL_TYPE_Country-wide_sum  \\\n",
       "0                                     0.0   \n",
       "1                                     1.0   \n",
       "2                                     0.0   \n",
       "3                                     1.0   \n",
       "4                                     3.0   \n",
       "\n",
       "   prev_app_CHANNEL_TYPE_Credit and cash offices_sum  \\\n",
       "0                                                0.0   \n",
       "1                                                1.0   \n",
       "2                                                0.0   \n",
       "3                                                7.0   \n",
       "4                                                1.0   \n",
       "\n",
       "   prev_app_CHANNEL_TYPE_Regional / Local_sum  \\\n",
       "0                                         0.0   \n",
       "1                                         0.0   \n",
       "2                                         1.0   \n",
       "3                                         0.0   \n",
       "4                                         1.0   \n",
       "\n",
       "   prev_app_CHANNEL_TYPE_Stone_sum  prev_app_SELLERPLACE_AREA_sum  \\\n",
       "0                              1.0                          500.0   \n",
       "1                              1.0                         1599.0   \n",
       "2                              0.0                           30.0   \n",
       "3                              1.0                         8048.0   \n",
       "4                              0.0                         2455.0   \n",
       "\n",
       "   prev_app_NAME_SELLER_INDUSTRY_Connectivity_sum  \\\n",
       "0                                             0.0   \n",
       "1                                             0.0   \n",
       "2                                             1.0   \n",
       "3                                             0.0   \n",
       "4                                             1.0   \n",
       "\n",
       "   prev_app_NAME_SELLER_INDUSTRY_Construction_sum  \\\n",
       "0                                             0.0   \n",
       "1                                             0.0   \n",
       "2                                             0.0   \n",
       "3                                             1.0   \n",
       "4                                             0.0   \n",
       "\n",
       "   prev_app_NAME_SELLER_INDUSTRY_Consumer electronics_sum  \\\n",
       "0                                                0.0        \n",
       "1                                                1.0        \n",
       "2                                                0.0        \n",
       "3                                                1.0        \n",
       "4                                                3.0        \n",
       "\n",
       "   prev_app_NAME_SELLER_INDUSTRY_Industry_sum  \\\n",
       "0                                         0.0   \n",
       "1                                         0.0   \n",
       "2                                         0.0   \n",
       "3                                         0.0   \n",
       "4                                         0.0   \n",
       "\n",
       "   prev_app_NAME_SELLER_INDUSTRY_MLM partners_sum  \\\n",
       "0                                             0.0   \n",
       "1                                             0.0   \n",
       "2                                             0.0   \n",
       "3                                             0.0   \n",
       "4                                             0.0   \n",
       "\n",
       "   prev_app_NAME_YIELD_GROUP_high_sum  \\\n",
       "0                                 0.0   \n",
       "1                                 0.0   \n",
       "2                                 0.0   \n",
       "3                                 2.0   \n",
       "4                                 3.0   \n",
       "\n",
       "   prev_app_NAME_YIELD_GROUP_low_action_sum  \\\n",
       "0                                       0.0   \n",
       "1                                       0.0   \n",
       "2                                       0.0   \n",
       "3                                       0.0   \n",
       "4                                       0.0   \n",
       "\n",
       "   prev_app_NAME_YIELD_GROUP_low_normal_sum  \\\n",
       "0                                       1.0   \n",
       "1                                       1.0   \n",
       "2                                       0.0   \n",
       "3                                       2.0   \n",
       "4                                       0.0   \n",
       "\n",
       "   prev_app_NAME_YIELD_GROUP_middle_sum  install_payments_AMT_INSTALMENT_mean  \\\n",
       "0                                   0.0                          11559.247105   \n",
       "1                                   2.0                          64754.586000   \n",
       "2                                   1.0                           7096.155000   \n",
       "3                                   1.0                          62947.088438   \n",
       "4                                   3.0                          12666.444545   \n",
       "\n",
       "   NAME_CONTRACT_TYPE_Cash loans  NAME_CONTRACT_TYPE_Revolving loans  \\\n",
       "0                              1                                   0   \n",
       "1                              1                                   0   \n",
       "2                              0                                   1   \n",
       "3                              1                                   0   \n",
       "4                              1                                   0   \n",
       "\n",
       "   CODE_GENDER_F  CODE_GENDER_M  FLAG_OWN_CAR_N  FLAG_OWN_CAR_Y  \\\n",
       "0              0              1               1               0   \n",
       "1              1              0               1               0   \n",
       "2              0              1               0               1   \n",
       "3              1              0               1               0   \n",
       "4              0              1               1               0   \n",
       "\n",
       "   FLAG_OWN_REALTY_N  FLAG_OWN_REALTY_Y  NAME_TYPE_SUITE_Children  \\\n",
       "0                  0                  1                         0   \n",
       "1                  1                  0                         0   \n",
       "2                  0                  1                         0   \n",
       "3                  0                  1                         0   \n",
       "4                  0                  1                         0   \n",
       "\n",
       "   NAME_TYPE_SUITE_Family  NAME_TYPE_SUITE_Group of people  \\\n",
       "0                       0                                0   \n",
       "1                       1                                0   \n",
       "2                       0                                0   \n",
       "3                       0                                0   \n",
       "4                       0                                0   \n",
       "\n",
       "   NAME_TYPE_SUITE_Other_A  NAME_TYPE_SUITE_Other_B  \\\n",
       "0                        0                        0   \n",
       "1                        0                        0   \n",
       "2                        0                        0   \n",
       "3                        0                        0   \n",
       "4                        0                        0   \n",
       "\n",
       "   NAME_TYPE_SUITE_Spouse, partner  NAME_TYPE_SUITE_Unaccompanied  \\\n",
       "0                                0                              1   \n",
       "1                                0                              0   \n",
       "2                                0                              1   \n",
       "3                                0                              1   \n",
       "4                                0                              1   \n",
       "\n",
       "   NAME_INCOME_TYPE_Businessman  NAME_INCOME_TYPE_Commercial associate  \\\n",
       "0                             0                                      0   \n",
       "1                             0                                      0   \n",
       "2                             0                                      0   \n",
       "3                             0                                      0   \n",
       "4                             0                                      0   \n",
       "\n",
       "   NAME_INCOME_TYPE_Maternity leave  NAME_INCOME_TYPE_State servant  \\\n",
       "0                                 0                               0   \n",
       "1                                 0                               1   \n",
       "2                                 0                               0   \n",
       "3                                 0                               0   \n",
       "4                                 0                               0   \n",
       "\n",
       "   NAME_INCOME_TYPE_Student  NAME_INCOME_TYPE_Unemployed  \\\n",
       "0                         0                            0   \n",
       "1                         0                            0   \n",
       "2                         0                            0   \n",
       "3                         0                            0   \n",
       "4                         0                            0   \n",
       "\n",
       "   NAME_INCOME_TYPE_Working  NAME_EDUCATION_TYPE_Academic degree  \\\n",
       "0                         1                                    0   \n",
       "1                         0                                    0   \n",
       "2                         1                                    0   \n",
       "3                         1                                    0   \n",
       "4                         1                                    0   \n",
       "\n",
       "   NAME_EDUCATION_TYPE_Higher education  \\\n",
       "0                                     0   \n",
       "1                                     1   \n",
       "2                                     0   \n",
       "3                                     0   \n",
       "4                                     0   \n",
       "\n",
       "   NAME_EDUCATION_TYPE_Incomplete higher  NAME_EDUCATION_TYPE_Lower secondary  \\\n",
       "0                                      0                                    0   \n",
       "1                                      0                                    0   \n",
       "2                                      0                                    0   \n",
       "3                                      0                                    0   \n",
       "4                                      0                                    0   \n",
       "\n",
       "   NAME_EDUCATION_TYPE_Secondary / secondary special  \\\n",
       "0                                                  1   \n",
       "1                                                  0   \n",
       "2                                                  1   \n",
       "3                                                  1   \n",
       "4                                                  1   \n",
       "\n",
       "   NAME_FAMILY_STATUS_Civil marriage  NAME_FAMILY_STATUS_Married  \\\n",
       "0                                  0                           0   \n",
       "1                                  0                           1   \n",
       "2                                  0                           0   \n",
       "3                                  1                           0   \n",
       "4                                  0                           0   \n",
       "\n",
       "   NAME_FAMILY_STATUS_Separated  NAME_FAMILY_STATUS_Single / not married  \\\n",
       "0                             0                                        1   \n",
       "1                             0                                        0   \n",
       "2                             0                                        1   \n",
       "3                             0                                        0   \n",
       "4                             0                                        1   \n",
       "\n",
       "   NAME_FAMILY_STATUS_Widow  NAME_HOUSING_TYPE_Co-op apartment  \\\n",
       "0                         0                                  0   \n",
       "1                         0                                  0   \n",
       "2                         0                                  0   \n",
       "3                         0                                  0   \n",
       "4                         0                                  0   \n",
       "\n",
       "   NAME_HOUSING_TYPE_House / apartment  NAME_HOUSING_TYPE_Municipal apartment  \\\n",
       "0                                    1                                      0   \n",
       "1                                    1                                      0   \n",
       "2                                    1                                      0   \n",
       "3                                    1                                      0   \n",
       "4                                    1                                      0   \n",
       "\n",
       "   NAME_HOUSING_TYPE_Office apartment  NAME_HOUSING_TYPE_Rented apartment  \\\n",
       "0                                   0                                   0   \n",
       "1                                   0                                   0   \n",
       "2                                   0                                   0   \n",
       "3                                   0                                   0   \n",
       "4                                   0                                   0   \n",
       "\n",
       "   NAME_HOUSING_TYPE_With parents  OCCUPATION_TYPE_Accountants  \\\n",
       "0                               0                            0   \n",
       "1                               0                            0   \n",
       "2                               0                            0   \n",
       "3                               0                            0   \n",
       "4                               0                            0   \n",
       "\n",
       "   OCCUPATION_TYPE_Cleaning staff  OCCUPATION_TYPE_Cooking staff  \\\n",
       "0                               0                              0   \n",
       "1                               0                              0   \n",
       "2                               0                              0   \n",
       "3                               0                              0   \n",
       "4                               0                              0   \n",
       "\n",
       "   OCCUPATION_TYPE_Core staff  OCCUPATION_TYPE_Drivers  \\\n",
       "0                           0                        0   \n",
       "1                           1                        0   \n",
       "2                           0                        0   \n",
       "3                           0                        0   \n",
       "4                           1                        0   \n",
       "\n",
       "   OCCUPATION_TYPE_HR staff  OCCUPATION_TYPE_High skill tech staff  \\\n",
       "0                         0                                      0   \n",
       "1                         0                                      0   \n",
       "2                         0                                      0   \n",
       "3                         0                                      0   \n",
       "4                         0                                      0   \n",
       "\n",
       "   OCCUPATION_TYPE_IT staff  OCCUPATION_TYPE_Laborers  \\\n",
       "0                         0                         1   \n",
       "1                         0                         0   \n",
       "2                         0                         1   \n",
       "3                         0                         1   \n",
       "4                         0                         0   \n",
       "\n",
       "   OCCUPATION_TYPE_Low-skill Laborers  OCCUPATION_TYPE_Managers  \\\n",
       "0                                   0                         0   \n",
       "1                                   0                         0   \n",
       "2                                   0                         0   \n",
       "3                                   0                         0   \n",
       "4                                   0                         0   \n",
       "\n",
       "   OCCUPATION_TYPE_Medicine staff  OCCUPATION_TYPE_Private service staff  \\\n",
       "0                               0                                      0   \n",
       "1                               0                                      0   \n",
       "2                               0                                      0   \n",
       "3                               0                                      0   \n",
       "4                               0                                      0   \n",
       "\n",
       "   OCCUPATION_TYPE_Realty agents  OCCUPATION_TYPE_Sales staff  \\\n",
       "0                              0                            0   \n",
       "1                              0                            0   \n",
       "2                              0                            0   \n",
       "3                              0                            0   \n",
       "4                              0                            0   \n",
       "\n",
       "   OCCUPATION_TYPE_Secretaries  OCCUPATION_TYPE_Security staff  \\\n",
       "0                            0                               0   \n",
       "1                            0                               0   \n",
       "2                            0                               0   \n",
       "3                            0                               0   \n",
       "4                            0                               0   \n",
       "\n",
       "   OCCUPATION_TYPE_Waiters/barmen staff  WEEKDAY_APPR_PROCESS_START_FRIDAY  \\\n",
       "0                                     0                                  0   \n",
       "1                                     0                                  0   \n",
       "2                                     0                                  0   \n",
       "3                                     0                                  0   \n",
       "4                                     0                                  0   \n",
       "\n",
       "   WEEKDAY_APPR_PROCESS_START_MONDAY  WEEKDAY_APPR_PROCESS_START_SATURDAY  \\\n",
       "0                                  0                                    0   \n",
       "1                                  1                                    0   \n",
       "2                                  1                                    0   \n",
       "3                                  0                                    0   \n",
       "4                                  0                                    0   \n",
       "\n",
       "   WEEKDAY_APPR_PROCESS_START_SUNDAY  WEEKDAY_APPR_PROCESS_START_THURSDAY  \\\n",
       "0                                  0                                    0   \n",
       "1                                  0                                    0   \n",
       "2                                  0                                    0   \n",
       "3                                  0                                    0   \n",
       "4                                  0                                    1   \n",
       "\n",
       "   WEEKDAY_APPR_PROCESS_START_TUESDAY  WEEKDAY_APPR_PROCESS_START_WEDNESDAY  \\\n",
       "0                                   0                                     1   \n",
       "1                                   0                                     0   \n",
       "2                                   0                                     0   \n",
       "3                                   0                                     1   \n",
       "4                                   0                                     0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Advertising  ORGANIZATION_TYPE_Agriculture  \\\n",
       "0                              0                              0   \n",
       "1                              0                              0   \n",
       "2                              0                              0   \n",
       "3                              0                              0   \n",
       "4                              0                              0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Bank  ORGANIZATION_TYPE_Business Entity Type 1  \\\n",
       "0                       0                                         0   \n",
       "1                       0                                         0   \n",
       "2                       0                                         0   \n",
       "3                       0                                         0   \n",
       "4                       0                                         0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Business Entity Type 2  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Business Entity Type 3  ORGANIZATION_TYPE_Cleaning  \\\n",
       "0                                         1                           0   \n",
       "1                                         0                           0   \n",
       "2                                         0                           0   \n",
       "3                                         1                           0   \n",
       "4                                         0                           0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Construction  ORGANIZATION_TYPE_Culture  \\\n",
       "0                               0                          0   \n",
       "1                               0                          0   \n",
       "2                               0                          0   \n",
       "3                               0                          0   \n",
       "4                               0                          0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Electricity  ORGANIZATION_TYPE_Emergency  \\\n",
       "0                              0                            0   \n",
       "1                              0                            0   \n",
       "2                              0                            0   \n",
       "3                              0                            0   \n",
       "4                              0                            0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Government  ORGANIZATION_TYPE_Hotel  \\\n",
       "0                             0                        0   \n",
       "1                             0                        0   \n",
       "2                             1                        0   \n",
       "3                             0                        0   \n",
       "4                             0                        0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Housing  ORGANIZATION_TYPE_Industry: type 1  \\\n",
       "0                          0                                   0   \n",
       "1                          0                                   0   \n",
       "2                          0                                   0   \n",
       "3                          0                                   0   \n",
       "4                          0                                   0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Industry: type 10  ORGANIZATION_TYPE_Industry: type 11  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Industry: type 12  ORGANIZATION_TYPE_Industry: type 13  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Industry: type 2  ORGANIZATION_TYPE_Industry: type 3  \\\n",
       "0                                   0                                   0   \n",
       "1                                   0                                   0   \n",
       "2                                   0                                   0   \n",
       "3                                   0                                   0   \n",
       "4                                   0                                   0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Industry: type 4  ORGANIZATION_TYPE_Industry: type 5  \\\n",
       "0                                   0                                   0   \n",
       "1                                   0                                   0   \n",
       "2                                   0                                   0   \n",
       "3                                   0                                   0   \n",
       "4                                   0                                   0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Industry: type 6  ORGANIZATION_TYPE_Industry: type 7  \\\n",
       "0                                   0                                   0   \n",
       "1                                   0                                   0   \n",
       "2                                   0                                   0   \n",
       "3                                   0                                   0   \n",
       "4                                   0                                   0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Industry: type 8  ORGANIZATION_TYPE_Industry: type 9  \\\n",
       "0                                   0                                   0   \n",
       "1                                   0                                   0   \n",
       "2                                   0                                   0   \n",
       "3                                   0                                   0   \n",
       "4                                   0                                   0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Insurance  ORGANIZATION_TYPE_Kindergarten  \\\n",
       "0                            0                               0   \n",
       "1                            0                               0   \n",
       "2                            0                               0   \n",
       "3                            0                               0   \n",
       "4                            0                               0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Legal Services  ORGANIZATION_TYPE_Medicine  \\\n",
       "0                                 0                           0   \n",
       "1                                 0                           0   \n",
       "2                                 0                           0   \n",
       "3                                 0                           0   \n",
       "4                                 0                           0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Military  ORGANIZATION_TYPE_Mobile  \\\n",
       "0                           0                         0   \n",
       "1                           0                         0   \n",
       "2                           0                         0   \n",
       "3                           0                         0   \n",
       "4                           0                         0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Other  ORGANIZATION_TYPE_Police  \\\n",
       "0                        0                         0   \n",
       "1                        0                         0   \n",
       "2                        0                         0   \n",
       "3                        0                         0   \n",
       "4                        0                         0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Postal  ORGANIZATION_TYPE_Realtor  \\\n",
       "0                         0                          0   \n",
       "1                         0                          0   \n",
       "2                         0                          0   \n",
       "3                         0                          0   \n",
       "4                         0                          0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Religion  ORGANIZATION_TYPE_Restaurant  \\\n",
       "0                           0                             0   \n",
       "1                           0                             0   \n",
       "2                           0                             0   \n",
       "3                           0                             0   \n",
       "4                           1                             0   \n",
       "\n",
       "   ORGANIZATION_TYPE_School  ORGANIZATION_TYPE_Security  \\\n",
       "0                         0                           0   \n",
       "1                         1                           0   \n",
       "2                         0                           0   \n",
       "3                         0                           0   \n",
       "4                         0                           0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Security Ministries  ORGANIZATION_TYPE_Self-employed  \\\n",
       "0                                      0                                0   \n",
       "1                                      0                                0   \n",
       "2                                      0                                0   \n",
       "3                                      0                                0   \n",
       "4                                      0                                0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Services  ORGANIZATION_TYPE_Telecom  \\\n",
       "0                           0                          0   \n",
       "1                           0                          0   \n",
       "2                           0                          0   \n",
       "3                           0                          0   \n",
       "4                           0                          0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Trade: type 1  ORGANIZATION_TYPE_Trade: type 2  \\\n",
       "0                                0                                0   \n",
       "1                                0                                0   \n",
       "2                                0                                0   \n",
       "3                                0                                0   \n",
       "4                                0                                0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Trade: type 3  ORGANIZATION_TYPE_Trade: type 4  \\\n",
       "0                                0                                0   \n",
       "1                                0                                0   \n",
       "2                                0                                0   \n",
       "3                                0                                0   \n",
       "4                                0                                0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Trade: type 5  ORGANIZATION_TYPE_Trade: type 6  \\\n",
       "0                                0                                0   \n",
       "1                                0                                0   \n",
       "2                                0                                0   \n",
       "3                                0                                0   \n",
       "4                                0                                0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Trade: type 7  ORGANIZATION_TYPE_Transport: type 1  \\\n",
       "0                                0                                    0   \n",
       "1                                0                                    0   \n",
       "2                                0                                    0   \n",
       "3                                0                                    0   \n",
       "4                                0                                    0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Transport: type 2  ORGANIZATION_TYPE_Transport: type 3  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   ORGANIZATION_TYPE_Transport: type 4  ORGANIZATION_TYPE_University  \\\n",
       "0                                    0                             0   \n",
       "1                                    0                             0   \n",
       "2                                    0                             0   \n",
       "3                                    0                             0   \n",
       "4                                    0                             0   \n",
       "\n",
       "   FONDKAPREMONT_MODE_not specified  FONDKAPREMONT_MODE_org spec account  \\\n",
       "0                                 0                                    0   \n",
       "1                                 0                                    0   \n",
       "2                                 0                                    0   \n",
       "3                                 0                                    0   \n",
       "4                                 0                                    0   \n",
       "\n",
       "   FONDKAPREMONT_MODE_reg oper account  \\\n",
       "0                                    1   \n",
       "1                                    1   \n",
       "2                                    0   \n",
       "3                                    0   \n",
       "4                                    0   \n",
       "\n",
       "   FONDKAPREMONT_MODE_reg oper spec account  WALLSMATERIAL_MODE_Block  \\\n",
       "0                                         0                         0   \n",
       "1                                         0                         1   \n",
       "2                                         0                         0   \n",
       "3                                         0                         0   \n",
       "4                                         0                         0   \n",
       "\n",
       "   WALLSMATERIAL_MODE_Mixed  WALLSMATERIAL_MODE_Monolithic  \\\n",
       "0                         0                              0   \n",
       "1                         0                              0   \n",
       "2                         0                              0   \n",
       "3                         0                              0   \n",
       "4                         0                              0   \n",
       "\n",
       "   WALLSMATERIAL_MODE_Others  WALLSMATERIAL_MODE_Panel  \\\n",
       "0                          0                         0   \n",
       "1                          0                         0   \n",
       "2                          0                         0   \n",
       "3                          0                         0   \n",
       "4                          0                         0   \n",
       "\n",
       "   WALLSMATERIAL_MODE_Stone, brick  WALLSMATERIAL_MODE_Wooden  \n",
       "0                                1                          0  \n",
       "1                                0                          0  \n",
       "2                                0                          0  \n",
       "3                                0                          0  \n",
       "4                                0                          0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307505, 336)\n"
     ]
    }
   ],
   "source": [
    "print(raw_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_CURR                                                            int64\n",
       "TARGET                                                                int64\n",
       "CNT_CHILDREN                                                          int64\n",
       "AMT_INCOME_TOTAL                                                    float64\n",
       "AMT_CREDIT                                                          float64\n",
       "AMT_ANNUITY                                                         float64\n",
       "REGION_POPULATION_RELATIVE                                          float64\n",
       "DAYS_BIRTH                                                            int64\n",
       "DAYS_EMPLOYED                                                         int64\n",
       "DAYS_REGISTRATION                                                   float64\n",
       "DAYS_ID_PUBLISH                                                       int64\n",
       "OWN_CAR_AGE                                                         float64\n",
       "FLAG_EMP_PHONE                                                        int64\n",
       "FLAG_WORK_PHONE                                                       int64\n",
       "FLAG_PHONE                                                            int64\n",
       "FLAG_EMAIL                                                            int64\n",
       "REGION_RATING_CLIENT                                                  int64\n",
       "HOUR_APPR_PROCESS_START                                               int64\n",
       "REG_REGION_NOT_WORK_REGION                                            int64\n",
       "REG_CITY_NOT_LIVE_CITY                                                int64\n",
       "REG_CITY_NOT_WORK_CITY                                                int64\n",
       "LIVE_CITY_NOT_WORK_CITY                                               int64\n",
       "EXT_SOURCE_1                                                        float64\n",
       "EXT_SOURCE_2                                                        float64\n",
       "EXT_SOURCE_3                                                        float64\n",
       "APARTMENTS_AVG                                                      float64\n",
       "BASEMENTAREA_AVG                                                    float64\n",
       "YEARS_BEGINEXPLUATATION_AVG                                         float64\n",
       "YEARS_BUILD_AVG                                                     float64\n",
       "COMMONAREA_AVG                                                      float64\n",
       "ELEVATORS_AVG                                                       float64\n",
       "ENTRANCES_AVG                                                       float64\n",
       "FLOORSMAX_AVG                                                       float64\n",
       "FLOORSMIN_AVG                                                       float64\n",
       "LANDAREA_AVG                                                        float64\n",
       "NONLIVINGAPARTMENTS_AVG                                             float64\n",
       "NONLIVINGAREA_AVG                                                   float64\n",
       "OBS_30_CNT_SOCIAL_CIRCLE                                            float64\n",
       "DEF_30_CNT_SOCIAL_CIRCLE                                            float64\n",
       "DAYS_LAST_PHONE_CHANGE                                              float64\n",
       "AMT_REQ_CREDIT_BUREAU_MON                                           float64\n",
       "AMT_REQ_CREDIT_BUREAU_QRT                                           float64\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR                                          float64\n",
       "bureau_DAYS_CREDIT_mean                                             float64\n",
       "bureau_CREDIT_DAY_OVERDUE_mean                                      float64\n",
       "bureau_DAYS_CREDIT_ENDDATE_min                                      float64\n",
       "bureau_DAYS_ENDDATE_FACT_max                                        float64\n",
       "bureau_AMT_CREDIT_MAX_OVERDUE_max                                   float64\n",
       "bureau_CNT_CREDIT_PROLONG_max                                       float64\n",
       "bureau_AMT_CREDIT_SUM_min                                           float64\n",
       "bureau_AMT_CREDIT_SUM_mean                                          float64\n",
       "bureau_AMT_CREDIT_SUM_max                                           float64\n",
       "bureau_AMT_CREDIT_SUM_var                                           float64\n",
       "bureau_AMT_CREDIT_SUM_DEBT_mean                                     float64\n",
       "bureau_AMT_CREDIT_SUM_LIMIT_max                                     float64\n",
       "bureau_AMT_CREDIT_SUM_OVERDUE_max                                   float64\n",
       "bureau_DAYS_CREDIT_UPDATE_max                                       float64\n",
       "bureau_AMT_ANNUITY_min                                              float64\n",
       "bureau_AMT_ANNUITY_mean                                             float64\n",
       "bureau_AMT_ANNUITY_max                                              float64\n",
       "bureau_CREDIT_TYPE_Car loan_sum                                     float64\n",
       "bureau_CREDIT_TYPE_Consumer credit_sum                              float64\n",
       "bureau_CREDIT_TYPE_Credit card_sum                                  float64\n",
       "bureau_CREDIT_TYPE_Microloan_sum                                    float64\n",
       "bureau_CREDIT_TYPE_Mortgage_sum                                     float64\n",
       "credit_card_MONTHS_BALANCE_max                                      float64\n",
       "credit_card_MONTHS_BALANCE_mean                                     float64\n",
       "credit_card_MONTHS_BALANCE_std                                      float64\n",
       "credit_card_AMT_BALANCE_max                                         float64\n",
       "credit_card_AMT_BALANCE_sum                                         float64\n",
       "credit_card_AMT_CREDIT_LIMIT_ACTUAL_max                             float64\n",
       "credit_card_AMT_CREDIT_LIMIT_ACTUAL_sum                             float64\n",
       "credit_card_AMT_CREDIT_LIMIT_ACTUAL_std                             float64\n",
       "credit_card_AMT_DRAWINGS_ATM_CURRENT_max                            float64\n",
       "credit_card_AMT_DRAWINGS_ATM_CURRENT_mean                           float64\n",
       "credit_card_AMT_DRAWINGS_ATM_CURRENT_sum                            float64\n",
       "credit_card_AMT_DRAWINGS_ATM_CURRENT_median                         float64\n",
       "credit_card_AMT_DRAWINGS_CURRENT_max                                float64\n",
       "credit_card_AMT_DRAWINGS_CURRENT_mean                               float64\n",
       "credit_card_AMT_DRAWINGS_CURRENT_sum                                float64\n",
       "credit_card_AMT_DRAWINGS_CURRENT_median                             float64\n",
       "credit_card_AMT_DRAWINGS_OTHER_CURRENT_max                          float64\n",
       "credit_card_AMT_DRAWINGS_OTHER_CURRENT_mean                         float64\n",
       "credit_card_AMT_DRAWINGS_OTHER_CURRENT_median                       float64\n",
       "credit_card_AMT_DRAWINGS_POS_CURRENT_max                            float64\n",
       "credit_card_AMT_DRAWINGS_POS_CURRENT_mean                           float64\n",
       "credit_card_AMT_DRAWINGS_POS_CURRENT_sum                            float64\n",
       "credit_card_AMT_PAYMENT_CURRENT_max                                 float64\n",
       "credit_card_AMT_PAYMENT_CURRENT_median                              float64\n",
       "credit_card_CNT_DRAWINGS_ATM_CURRENT_max                            float64\n",
       "credit_card_CNT_DRAWINGS_ATM_CURRENT_mean                           float64\n",
       "credit_card_CNT_DRAWINGS_ATM_CURRENT_sum                            float64\n",
       "credit_card_CNT_DRAWINGS_ATM_CURRENT_median                         float64\n",
       "credit_card_CNT_DRAWINGS_CURRENT_max                                float64\n",
       "credit_card_CNT_DRAWINGS_CURRENT_mean                               float64\n",
       "credit_card_CNT_DRAWINGS_CURRENT_sum                                float64\n",
       "credit_card_CNT_DRAWINGS_OTHER_CURRENT_max                          float64\n",
       "credit_card_CNT_DRAWINGS_OTHER_CURRENT_mean                         float64\n",
       "credit_card_CNT_DRAWINGS_OTHER_CURRENT_sum                          float64\n",
       "credit_card_CNT_INSTALMENT_MATURE_CUM_max                           float64\n",
       "credit_card_SK_DPD_max                                              float64\n",
       "credit_card_SK_DPD_DEF_max                                          float64\n",
       "credit_card_PNT_BALANCE_DRAWINGS_ATM_max                            float64\n",
       "credit_card_PNT_BALANCE_DRAWINGS_ATM_mean                           float64\n",
       "credit_card_PNT_BALANCE_DRAWINGS_ATM_median                         float64\n",
       "credit_card_PNT_BALANCE_DRAWINGS_ATM_std                            float64\n",
       "credit_card_AVG_DRAWINGS_ATM_CURRENT_max                            float64\n",
       "credit_card_AVG_DRAWINGS_ATM_CURRENT_mean                           float64\n",
       "credit_card_AVG_DRAWINGS_POS_CURRENT_max                            float64\n",
       "credit_card_AVG_DRAWINGS_POS_CURRENT_mean                           float64\n",
       "credit_card_AVG_DRAWINGS_POS_CURRENT_sum                            float64\n",
       "cash_balance_CNT_INSTALMENT_min                                     float64\n",
       "cash_balance_CNT_INSTALMENT_mean                                    float64\n",
       "cash_balance_CNT_INSTALMENT_max                                     float64\n",
       "cash_balance_CNT_INSTALMENT_FUTURE_min                              float64\n",
       "cash_balance_SK_DPD_min                                             float64\n",
       "cash_balance_SK_DPD_mean                                            float64\n",
       "cash_balance_SK_DPD_DEF_mean                                        float64\n",
       "prev_app_AMT_ANNUITY_max                                            float64\n",
       "prev_app_AMT_ANNUITY_mean                                           float64\n",
       "prev_app_AMT_ANNUITY_min                                            float64\n",
       "prev_app_AMT_APPLICATION_max                                        float64\n",
       "prev_app_AMT_APPLICATION_mean                                       float64\n",
       "prev_app_AMT_APPLICATION_min                                        float64\n",
       "prev_app_AMT_DOWN_PAYMENT_max                                       float64\n",
       "prev_app_AMT_DOWN_PAYMENT_min                                       float64\n",
       "prev_app_AMT_GOODS_PRICE_min                                        float64\n",
       "prev_app_RATE_DOWN_PAYMENT_max                                      float64\n",
       "prev_app_RATE_DOWN_PAYMENT_mean                                     float64\n",
       "prev_app_RATE_DOWN_PAYMENT_min                                      float64\n",
       "prev_app_DAYS_DECISION_max                                          float64\n",
       "prev_app_DAYS_DECISION_mean                                         float64\n",
       "prev_app_DAYS_DECISION_min                                          float64\n",
       "prev_app_DAYS_DECISION_std                                          float64\n",
       "prev_app_CNT_PAYMENT_max                                            float64\n",
       "prev_app_CNT_PAYMENT_mean                                           float64\n",
       "prev_app_CNT_PAYMENT_min                                            float64\n",
       "prev_app_DAYS_FIRST_DUE_max                                         float64\n",
       "prev_app_DAYS_LAST_DUE_1ST_VERSION_max                              float64\n",
       "prev_app_DAYS_LAST_DUE_1ST_VERSION_std                              float64\n",
       "prev_app_DAYS_LAST_DUE_max                                          float64\n",
       "prev_app_DAYS_LAST_DUE_std                                          float64\n",
       "prev_app_DOWN_PAYMENT_PNT_var                                       float64\n",
       "prev_app_ANNUITY_CREDIT_RATIO_max                                   float64\n",
       "prev_app_ANNUITY_CREDIT_RATIO_mean                                  float64\n",
       "prev_app_ANNUITY_CREDIT_RATIO_min                                   float64\n",
       "prev_app_ANNUITY_CREDIT_RATIO_std                                   float64\n",
       "prev_app_NAME_CASH_LOAN_PURPOSE_Building a house or an annex_sum    float64\n",
       "prev_app_NAME_CASH_LOAN_PURPOSE_Buying a new car_sum                float64\n",
       "prev_app_NAME_CASH_LOAN_PURPOSE_Buying a used car_sum               float64\n",
       "prev_app_NAME_CASH_LOAN_PURPOSE_Medicine_sum                        float64\n",
       "prev_app_NAME_CASH_LOAN_PURPOSE_Other_sum                           float64\n",
       "prev_app_NAME_CASH_LOAN_PURPOSE_Repairs_sum                         float64\n",
       "prev_app_NAME_CASH_LOAN_PURPOSE_Urgent needs_sum                    float64\n",
       "prev_app_NAME_CASH_LOAN_PURPOSE_XAP_sum                             float64\n",
       "prev_app_NAME_PAYMENT_TYPE_Cash through the bank_sum                float64\n",
       "prev_app_NAME_PAYMENT_TYPE_Non-cash from your account_sum           float64\n",
       "prev_app_WEEKDAY_APPR_PROCESS_START_FRIDAY_sum                      float64\n",
       "prev_app_WEEKDAY_APPR_PROCESS_START_MONDAY_sum                      float64\n",
       "prev_app_WEEKDAY_APPR_PROCESS_START_SATURDAY_sum                    float64\n",
       "prev_app_WEEKDAY_APPR_PROCESS_START_SUNDAY_sum                      float64\n",
       "prev_app_WEEKDAY_APPR_PROCESS_START_THURSDAY_sum                    float64\n",
       "prev_app_WEEKDAY_APPR_PROCESS_START_TUESDAY_sum                     float64\n",
       "prev_app_WEEKDAY_APPR_PROCESS_START_WEDNESDAY_sum                   float64\n",
       "prev_app_CODE_REJECT_REASON_CLIENT_sum                              float64\n",
       "prev_app_CODE_REJECT_REASON_HC_sum                                  float64\n",
       "prev_app_CODE_REJECT_REASON_LIMIT_sum                               float64\n",
       "prev_app_CODE_REJECT_REASON_SCO_sum                                 float64\n",
       "prev_app_CODE_REJECT_REASON_SCOFR_sum                               float64\n",
       "prev_app_CODE_REJECT_REASON_SYSTEM_sum                              float64\n",
       "prev_app_CODE_REJECT_REASON_XAP_sum                                 float64\n",
       "prev_app_NAME_CLIENT_TYPE_New_sum                                   float64\n",
       "prev_app_NAME_CLIENT_TYPE_Refreshed_sum                             float64\n",
       "prev_app_NAME_GOODS_CATEGORY_Audio/Video_sum                        float64\n",
       "prev_app_NAME_GOODS_CATEGORY_Auto Accessories_sum                   float64\n",
       "prev_app_NAME_GOODS_CATEGORY_Clothing and Accessories_sum           float64\n",
       "prev_app_NAME_GOODS_CATEGORY_Computers_sum                          float64\n",
       "prev_app_NAME_GOODS_CATEGORY_Construction Materials_sum             float64\n",
       "prev_app_NAME_GOODS_CATEGORY_Consumer Electronics_sum               float64\n",
       "prev_app_NAME_GOODS_CATEGORY_Furniture_sum                          float64\n",
       "prev_app_NAME_GOODS_CATEGORY_Homewares_sum                          float64\n",
       "prev_app_NAME_GOODS_CATEGORY_Mobile_sum                             float64\n",
       "prev_app_NAME_GOODS_CATEGORY_Photo / Cinema Equipment_sum           float64\n",
       "prev_app_NAME_GOODS_CATEGORY_Tourism_sum                            float64\n",
       "prev_app_NAME_PORTFOLIO_Cards_sum                                   float64\n",
       "prev_app_NAME_PORTFOLIO_Cash_sum                                    float64\n",
       "prev_app_NAME_PRODUCT_TYPE_walk-in_sum                              float64\n",
       "prev_app_CHANNEL_TYPE_AP+ (Cash loan)_sum                           float64\n",
       "prev_app_CHANNEL_TYPE_Channel of corporate sales_sum                float64\n",
       "prev_app_CHANNEL_TYPE_Contact center_sum                            float64\n",
       "prev_app_CHANNEL_TYPE_Country-wide_sum                              float64\n",
       "prev_app_CHANNEL_TYPE_Credit and cash offices_sum                   float64\n",
       "prev_app_CHANNEL_TYPE_Regional / Local_sum                          float64\n",
       "prev_app_CHANNEL_TYPE_Stone_sum                                     float64\n",
       "prev_app_SELLERPLACE_AREA_sum                                       float64\n",
       "prev_app_NAME_SELLER_INDUSTRY_Connectivity_sum                      float64\n",
       "prev_app_NAME_SELLER_INDUSTRY_Construction_sum                      float64\n",
       "prev_app_NAME_SELLER_INDUSTRY_Consumer electronics_sum              float64\n",
       "prev_app_NAME_SELLER_INDUSTRY_Industry_sum                          float64\n",
       "prev_app_NAME_SELLER_INDUSTRY_MLM partners_sum                      float64\n",
       "prev_app_NAME_YIELD_GROUP_high_sum                                  float64\n",
       "prev_app_NAME_YIELD_GROUP_low_action_sum                            float64\n",
       "prev_app_NAME_YIELD_GROUP_low_normal_sum                            float64\n",
       "prev_app_NAME_YIELD_GROUP_middle_sum                                float64\n",
       "install_payments_AMT_INSTALMENT_mean                                float64\n",
       "NAME_CONTRACT_TYPE_Cash loans                                         int64\n",
       "NAME_CONTRACT_TYPE_Revolving loans                                    int64\n",
       "CODE_GENDER_F                                                         int64\n",
       "CODE_GENDER_M                                                         int64\n",
       "FLAG_OWN_CAR_N                                                        int64\n",
       "FLAG_OWN_CAR_Y                                                        int64\n",
       "FLAG_OWN_REALTY_N                                                     int64\n",
       "FLAG_OWN_REALTY_Y                                                     int64\n",
       "NAME_TYPE_SUITE_Children                                              int64\n",
       "NAME_TYPE_SUITE_Family                                                int64\n",
       "NAME_TYPE_SUITE_Group of people                                       int64\n",
       "NAME_TYPE_SUITE_Other_A                                               int64\n",
       "NAME_TYPE_SUITE_Other_B                                               int64\n",
       "NAME_TYPE_SUITE_Spouse, partner                                       int64\n",
       "NAME_TYPE_SUITE_Unaccompanied                                         int64\n",
       "NAME_INCOME_TYPE_Businessman                                          int64\n",
       "NAME_INCOME_TYPE_Commercial associate                                 int64\n",
       "NAME_INCOME_TYPE_Maternity leave                                      int64\n",
       "NAME_INCOME_TYPE_State servant                                        int64\n",
       "NAME_INCOME_TYPE_Student                                              int64\n",
       "NAME_INCOME_TYPE_Unemployed                                           int64\n",
       "NAME_INCOME_TYPE_Working                                              int64\n",
       "NAME_EDUCATION_TYPE_Academic degree                                   int64\n",
       "NAME_EDUCATION_TYPE_Higher education                                  int64\n",
       "NAME_EDUCATION_TYPE_Incomplete higher                                 int64\n",
       "NAME_EDUCATION_TYPE_Lower secondary                                   int64\n",
       "NAME_EDUCATION_TYPE_Secondary / secondary special                     int64\n",
       "NAME_FAMILY_STATUS_Civil marriage                                     int64\n",
       "NAME_FAMILY_STATUS_Married                                            int64\n",
       "NAME_FAMILY_STATUS_Separated                                          int64\n",
       "NAME_FAMILY_STATUS_Single / not married                               int64\n",
       "NAME_FAMILY_STATUS_Widow                                              int64\n",
       "NAME_HOUSING_TYPE_Co-op apartment                                     int64\n",
       "NAME_HOUSING_TYPE_House / apartment                                   int64\n",
       "NAME_HOUSING_TYPE_Municipal apartment                                 int64\n",
       "NAME_HOUSING_TYPE_Office apartment                                    int64\n",
       "NAME_HOUSING_TYPE_Rented apartment                                    int64\n",
       "NAME_HOUSING_TYPE_With parents                                        int64\n",
       "OCCUPATION_TYPE_Accountants                                           int64\n",
       "OCCUPATION_TYPE_Cleaning staff                                        int64\n",
       "OCCUPATION_TYPE_Cooking staff                                         int64\n",
       "OCCUPATION_TYPE_Core staff                                            int64\n",
       "OCCUPATION_TYPE_Drivers                                               int64\n",
       "OCCUPATION_TYPE_HR staff                                              int64\n",
       "OCCUPATION_TYPE_High skill tech staff                                 int64\n",
       "OCCUPATION_TYPE_IT staff                                              int64\n",
       "OCCUPATION_TYPE_Laborers                                              int64\n",
       "OCCUPATION_TYPE_Low-skill Laborers                                    int64\n",
       "OCCUPATION_TYPE_Managers                                              int64\n",
       "OCCUPATION_TYPE_Medicine staff                                        int64\n",
       "OCCUPATION_TYPE_Private service staff                                 int64\n",
       "OCCUPATION_TYPE_Realty agents                                         int64\n",
       "OCCUPATION_TYPE_Sales staff                                           int64\n",
       "OCCUPATION_TYPE_Secretaries                                           int64\n",
       "OCCUPATION_TYPE_Security staff                                        int64\n",
       "OCCUPATION_TYPE_Waiters/barmen staff                                  int64\n",
       "WEEKDAY_APPR_PROCESS_START_FRIDAY                                     int64\n",
       "WEEKDAY_APPR_PROCESS_START_MONDAY                                     int64\n",
       "WEEKDAY_APPR_PROCESS_START_SATURDAY                                   int64\n",
       "WEEKDAY_APPR_PROCESS_START_SUNDAY                                     int64\n",
       "WEEKDAY_APPR_PROCESS_START_THURSDAY                                   int64\n",
       "WEEKDAY_APPR_PROCESS_START_TUESDAY                                    int64\n",
       "WEEKDAY_APPR_PROCESS_START_WEDNESDAY                                  int64\n",
       "ORGANIZATION_TYPE_Advertising                                         int64\n",
       "ORGANIZATION_TYPE_Agriculture                                         int64\n",
       "ORGANIZATION_TYPE_Bank                                                int64\n",
       "ORGANIZATION_TYPE_Business Entity Type 1                              int64\n",
       "ORGANIZATION_TYPE_Business Entity Type 2                              int64\n",
       "ORGANIZATION_TYPE_Business Entity Type 3                              int64\n",
       "ORGANIZATION_TYPE_Cleaning                                            int64\n",
       "ORGANIZATION_TYPE_Construction                                        int64\n",
       "ORGANIZATION_TYPE_Culture                                             int64\n",
       "ORGANIZATION_TYPE_Electricity                                         int64\n",
       "ORGANIZATION_TYPE_Emergency                                           int64\n",
       "ORGANIZATION_TYPE_Government                                          int64\n",
       "ORGANIZATION_TYPE_Hotel                                               int64\n",
       "ORGANIZATION_TYPE_Housing                                             int64\n",
       "ORGANIZATION_TYPE_Industry: type 1                                    int64\n",
       "ORGANIZATION_TYPE_Industry: type 10                                   int64\n",
       "ORGANIZATION_TYPE_Industry: type 11                                   int64\n",
       "ORGANIZATION_TYPE_Industry: type 12                                   int64\n",
       "ORGANIZATION_TYPE_Industry: type 13                                   int64\n",
       "ORGANIZATION_TYPE_Industry: type 2                                    int64\n",
       "ORGANIZATION_TYPE_Industry: type 3                                    int64\n",
       "ORGANIZATION_TYPE_Industry: type 4                                    int64\n",
       "ORGANIZATION_TYPE_Industry: type 5                                    int64\n",
       "ORGANIZATION_TYPE_Industry: type 6                                    int64\n",
       "ORGANIZATION_TYPE_Industry: type 7                                    int64\n",
       "ORGANIZATION_TYPE_Industry: type 8                                    int64\n",
       "ORGANIZATION_TYPE_Industry: type 9                                    int64\n",
       "ORGANIZATION_TYPE_Insurance                                           int64\n",
       "ORGANIZATION_TYPE_Kindergarten                                        int64\n",
       "ORGANIZATION_TYPE_Legal Services                                      int64\n",
       "ORGANIZATION_TYPE_Medicine                                            int64\n",
       "ORGANIZATION_TYPE_Military                                            int64\n",
       "ORGANIZATION_TYPE_Mobile                                              int64\n",
       "ORGANIZATION_TYPE_Other                                               int64\n",
       "ORGANIZATION_TYPE_Police                                              int64\n",
       "ORGANIZATION_TYPE_Postal                                              int64\n",
       "ORGANIZATION_TYPE_Realtor                                             int64\n",
       "ORGANIZATION_TYPE_Religion                                            int64\n",
       "ORGANIZATION_TYPE_Restaurant                                          int64\n",
       "ORGANIZATION_TYPE_School                                              int64\n",
       "ORGANIZATION_TYPE_Security                                            int64\n",
       "ORGANIZATION_TYPE_Security Ministries                                 int64\n",
       "ORGANIZATION_TYPE_Self-employed                                       int64\n",
       "ORGANIZATION_TYPE_Services                                            int64\n",
       "ORGANIZATION_TYPE_Telecom                                             int64\n",
       "ORGANIZATION_TYPE_Trade: type 1                                       int64\n",
       "ORGANIZATION_TYPE_Trade: type 2                                       int64\n",
       "ORGANIZATION_TYPE_Trade: type 3                                       int64\n",
       "ORGANIZATION_TYPE_Trade: type 4                                       int64\n",
       "ORGANIZATION_TYPE_Trade: type 5                                       int64\n",
       "ORGANIZATION_TYPE_Trade: type 6                                       int64\n",
       "ORGANIZATION_TYPE_Trade: type 7                                       int64\n",
       "ORGANIZATION_TYPE_Transport: type 1                                   int64\n",
       "ORGANIZATION_TYPE_Transport: type 2                                   int64\n",
       "ORGANIZATION_TYPE_Transport: type 3                                   int64\n",
       "ORGANIZATION_TYPE_Transport: type 4                                   int64\n",
       "ORGANIZATION_TYPE_University                                          int64\n",
       "FONDKAPREMONT_MODE_not specified                                      int64\n",
       "FONDKAPREMONT_MODE_org spec account                                   int64\n",
       "FONDKAPREMONT_MODE_reg oper account                                   int64\n",
       "FONDKAPREMONT_MODE_reg oper spec account                              int64\n",
       "WALLSMATERIAL_MODE_Block                                              int64\n",
       "WALLSMATERIAL_MODE_Mixed                                              int64\n",
       "WALLSMATERIAL_MODE_Monolithic                                         int64\n",
       "WALLSMATERIAL_MODE_Others                                             int64\n",
       "WALLSMATERIAL_MODE_Panel                                              int64\n",
       "WALLSMATERIAL_MODE_Stone, brick                                       int64\n",
       "WALLSMATERIAL_MODE_Wooden                                             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "credit_card_AVG_DRAWINGS_ATM_CURRENT_mean                           91.76%\n",
      "credit_card_AVG_DRAWINGS_ATM_CURRENT_max                            91.76%\n",
      "credit_card_AVG_DRAWINGS_POS_CURRENT_mean                           88.88%\n",
      "credit_card_AVG_DRAWINGS_POS_CURRENT_max                            88.88%\n",
      "credit_card_PNT_BALANCE_DRAWINGS_ATM_std                            81.52%\n",
      "credit_card_PNT_BALANCE_DRAWINGS_ATM_mean                           81.07%\n",
      "credit_card_PNT_BALANCE_DRAWINGS_ATM_max                            81.07%\n",
      "credit_card_PNT_BALANCE_DRAWINGS_ATM_median                         80.68%\n",
      "credit_card_AMT_PAYMENT_CURRENT_median                              80.14%\n",
      "credit_card_AMT_PAYMENT_CURRENT_max                                 80.14%\n",
      "credit_card_AMT_DRAWINGS_ATM_CURRENT_max                            80.12%\n",
      "credit_card_AMT_DRAWINGS_ATM_CURRENT_mean                           80.12%\n",
      "credit_card_AMT_DRAWINGS_ATM_CURRENT_median                         80.12%\n",
      "credit_card_AMT_DRAWINGS_OTHER_CURRENT_max                          80.12%\n",
      "credit_card_AMT_DRAWINGS_OTHER_CURRENT_mean                         80.12%\n",
      "credit_card_AMT_DRAWINGS_POS_CURRENT_max                            80.12%\n",
      "credit_card_AMT_DRAWINGS_OTHER_CURRENT_median                       80.12%\n",
      "credit_card_CNT_DRAWINGS_ATM_CURRENT_median                         80.12%\n",
      "credit_card_AMT_DRAWINGS_POS_CURRENT_mean                           80.12%\n",
      "credit_card_CNT_DRAWINGS_ATM_CURRENT_max                            80.12%\n",
      "credit_card_CNT_DRAWINGS_ATM_CURRENT_mean                           80.12%\n",
      "credit_card_CNT_DRAWINGS_OTHER_CURRENT_max                          80.12%\n",
      "credit_card_CNT_DRAWINGS_OTHER_CURRENT_mean                         80.12%\n",
      "bureau_AMT_ANNUITY_min                                              73.98%\n",
      "bureau_AMT_ANNUITY_mean                                             73.98%\n",
      "bureau_AMT_ANNUITY_max                                              73.98%\n",
      "credit_card_AMT_CREDIT_LIMIT_ACTUAL_std                             71.94%\n",
      "credit_card_MONTHS_BALANCE_std                                      71.94%\n",
      "credit_card_MONTHS_BALANCE_max                                      71.74%\n",
      "credit_card_AMT_BALANCE_max                                         71.74%\n",
      "credit_card_AMT_BALANCE_sum                                         71.74%\n",
      "credit_card_AMT_CREDIT_LIMIT_ACTUAL_max                             71.74%\n",
      "credit_card_AMT_CREDIT_LIMIT_ACTUAL_sum                             71.74%\n",
      "credit_card_AMT_DRAWINGS_ATM_CURRENT_sum                            71.74%\n",
      "credit_card_AMT_DRAWINGS_CURRENT_max                                71.74%\n",
      "credit_card_AMT_DRAWINGS_CURRENT_mean                               71.74%\n",
      "credit_card_AMT_DRAWINGS_CURRENT_sum                                71.74%\n",
      "credit_card_AMT_DRAWINGS_CURRENT_median                             71.74%\n",
      "credit_card_AMT_DRAWINGS_POS_CURRENT_sum                            71.74%\n",
      "credit_card_CNT_DRAWINGS_ATM_CURRENT_sum                            71.74%\n",
      "credit_card_CNT_DRAWINGS_CURRENT_max                                71.74%\n",
      "credit_card_CNT_DRAWINGS_CURRENT_mean                               71.74%\n",
      "credit_card_CNT_DRAWINGS_CURRENT_sum                                71.74%\n",
      "credit_card_CNT_DRAWINGS_OTHER_CURRENT_sum                          71.74%\n",
      "credit_card_CNT_INSTALMENT_MATURE_CUM_max                           71.74%\n",
      "credit_card_SK_DPD_max                                              71.74%\n",
      "credit_card_SK_DPD_DEF_max                                          71.74%\n",
      "credit_card_AVG_DRAWINGS_POS_CURRENT_sum                            71.74%\n",
      "credit_card_MONTHS_BALANCE_mean                                     71.74%\n",
      "COMMONAREA_AVG                                                      69.87%\n",
      "NONLIVINGAPARTMENTS_AVG                                             69.43%\n",
      "FLOORSMIN_AVG                                                       67.85%\n",
      "YEARS_BUILD_AVG                                                      66.5%\n",
      "OWN_CAR_AGE                                                         65.99%\n",
      "prev_app_DAYS_LAST_DUE_1ST_VERSION_max                               6.66%\n",
      "LANDAREA_AVG                                                        59.38%\n",
      "BASEMENTAREA_AVG                                                    58.52%\n",
      "EXT_SOURCE_1                                                        56.38%\n",
      "NONLIVINGAREA_AVG                                                   55.18%\n",
      "ELEVATORS_AVG                                                        53.3%\n",
      "APARTMENTS_AVG                                                      50.75%\n",
      "ENTRANCES_AVG                                                       50.35%\n",
      "prev_app_DAYS_FIRST_DUE_max                                          5.94%\n",
      "cash_balance_CNT_INSTALMENT_FUTURE_min                               5.88%\n",
      "cash_balance_CNT_INSTALMENT_max                                      5.88%\n",
      "cash_balance_CNT_INSTALMENT_mean                                     5.88%\n",
      "cash_balance_CNT_INSTALMENT_min                                      5.88%\n",
      "cash_balance_SK_DPD_mean                                             5.87%\n",
      "cash_balance_SK_DPD_min                                              5.87%\n",
      "cash_balance_SK_DPD_DEF_mean                                         5.87%\n",
      "prev_app_AMT_GOODS_PRICE_min                                         5.67%\n",
      "prev_app_AMT_ANNUITY_mean                                            5.49%\n",
      "prev_app_ANNUITY_CREDIT_RATIO_max                                    5.49%\n",
      "prev_app_AMT_ANNUITY_max                                             5.49%\n",
      "prev_app_ANNUITY_CREDIT_RATIO_min                                    5.49%\n",
      "prev_app_CNT_PAYMENT_max                                             5.49%\n",
      "prev_app_CNT_PAYMENT_mean                                            5.49%\n",
      "prev_app_CNT_PAYMENT_min                                             5.49%\n",
      "prev_app_AMT_ANNUITY_min                                             5.49%\n",
      "prev_app_ANNUITY_CREDIT_RATIO_mean                                   5.49%\n",
      "prev_app_NAME_GOODS_CATEGORY_Furniture_sum                           5.35%\n",
      "prev_app_NAME_CASH_LOAN_PURPOSE_Other_sum                            5.35%\n",
      "prev_app_CODE_REJECT_REASON_LIMIT_sum                                5.35%\n",
      "prev_app_CODE_REJECT_REASON_SCO_sum                                  5.35%\n",
      "prev_app_NAME_GOODS_CATEGORY_Computers_sum                           5.35%\n",
      "prev_app_CODE_REJECT_REASON_SYSTEM_sum                               5.35%\n",
      "prev_app_NAME_CLIENT_TYPE_Refreshed_sum                              5.35%\n",
      "prev_app_CODE_REJECT_REASON_XAP_sum                                  5.35%\n",
      "prev_app_NAME_CLIENT_TYPE_New_sum                                    5.35%\n",
      "prev_app_CODE_REJECT_REASON_HC_sum                                   5.35%\n",
      "prev_app_NAME_GOODS_CATEGORY_Audio/Video_sum                         5.35%\n",
      "prev_app_NAME_GOODS_CATEGORY_Homewares_sum                           5.35%\n",
      "prev_app_AMT_APPLICATION_max                                         5.35%\n",
      "prev_app_AMT_APPLICATION_mean                                        5.35%\n",
      "prev_app_AMT_APPLICATION_min                                         5.35%\n",
      "prev_app_NAME_GOODS_CATEGORY_Mobile_sum                              5.35%\n",
      "prev_app_NAME_CASH_LOAN_PURPOSE_Medicine_sum                         5.35%\n",
      "prev_app_DAYS_DECISION_max                                           5.35%\n",
      "prev_app_NAME_CASH_LOAN_PURPOSE_Buying a used car_sum                5.35%\n",
      "prev_app_NAME_CASH_LOAN_PURPOSE_Buying a new car_sum                 5.35%\n",
      "prev_app_NAME_CASH_LOAN_PURPOSE_Repairs_sum                          5.35%\n",
      "prev_app_NAME_CASH_LOAN_PURPOSE_XAP_sum                              5.35%\n",
      "prev_app_NAME_GOODS_CATEGORY_Construction Materials_sum              5.35%\n",
      "prev_app_DAYS_DECISION_min                                           5.35%\n",
      "prev_app_NAME_PAYMENT_TYPE_Cash through the bank_sum                 5.35%\n",
      "prev_app_NAME_GOODS_CATEGORY_Consumer Electronics_sum                5.35%\n",
      "prev_app_NAME_PAYMENT_TYPE_Non-cash from your account_sum            5.35%\n",
      "prev_app_WEEKDAY_APPR_PROCESS_START_FRIDAY_sum                       5.35%\n",
      "prev_app_WEEKDAY_APPR_PROCESS_START_MONDAY_sum                       5.35%\n",
      "prev_app_WEEKDAY_APPR_PROCESS_START_SATURDAY_sum                     5.35%\n",
      "prev_app_WEEKDAY_APPR_PROCESS_START_SUNDAY_sum                       5.35%\n",
      "prev_app_NAME_CASH_LOAN_PURPOSE_Urgent needs_sum                     5.35%\n",
      "prev_app_WEEKDAY_APPR_PROCESS_START_THURSDAY_sum                     5.35%\n",
      "prev_app_WEEKDAY_APPR_PROCESS_START_TUESDAY_sum                      5.35%\n",
      "prev_app_WEEKDAY_APPR_PROCESS_START_WEDNESDAY_sum                    5.35%\n",
      "prev_app_CODE_REJECT_REASON_CLIENT_sum                               5.35%\n",
      "prev_app_DAYS_DECISION_mean                                          5.35%\n",
      "prev_app_NAME_CASH_LOAN_PURPOSE_Building a house or an annex_sum     5.35%\n",
      "prev_app_CODE_REJECT_REASON_SCOFR_sum                                5.35%\n",
      "prev_app_NAME_GOODS_CATEGORY_Clothing and Accessories_sum            5.35%\n",
      "prev_app_CHANNEL_TYPE_Stone_sum                                      5.35%\n",
      "prev_app_NAME_PORTFOLIO_Cards_sum                                    5.35%\n",
      "prev_app_NAME_PORTFOLIO_Cash_sum                                     5.35%\n",
      "prev_app_NAME_PRODUCT_TYPE_walk-in_sum                               5.35%\n",
      "prev_app_CHANNEL_TYPE_AP+ (Cash loan)_sum                            5.35%\n",
      "prev_app_CHANNEL_TYPE_Channel of corporate sales_sum                 5.35%\n",
      "prev_app_CHANNEL_TYPE_Contact center_sum                             5.35%\n",
      "prev_app_CHANNEL_TYPE_Country-wide_sum                               5.35%\n",
      "prev_app_CHANNEL_TYPE_Credit and cash offices_sum                    5.35%\n",
      "prev_app_CHANNEL_TYPE_Regional / Local_sum                           5.35%\n",
      "prev_app_SELLERPLACE_AREA_sum                                        5.35%\n",
      "prev_app_NAME_GOODS_CATEGORY_Photo / Cinema Equipment_sum            5.35%\n",
      "prev_app_NAME_SELLER_INDUSTRY_Connectivity_sum                       5.35%\n",
      "prev_app_NAME_SELLER_INDUSTRY_Construction_sum                       5.35%\n",
      "prev_app_NAME_SELLER_INDUSTRY_Consumer electronics_sum               5.35%\n",
      "prev_app_NAME_SELLER_INDUSTRY_Industry_sum                           5.35%\n",
      "prev_app_NAME_SELLER_INDUSTRY_MLM partners_sum                       5.35%\n",
      "prev_app_NAME_YIELD_GROUP_high_sum                                   5.35%\n",
      "prev_app_NAME_YIELD_GROUP_low_action_sum                             5.35%\n",
      "prev_app_NAME_YIELD_GROUP_low_normal_sum                             5.35%\n",
      "prev_app_NAME_YIELD_GROUP_middle_sum                                 5.35%\n",
      "prev_app_NAME_GOODS_CATEGORY_Tourism_sum                             5.35%\n",
      "prev_app_NAME_GOODS_CATEGORY_Auto Accessories_sum                    5.35%\n",
      "install_payments_AMT_INSTALMENT_mean                                 5.16%\n",
      "FLOORSMAX_AVG                                                       49.76%\n",
      "YEARS_BEGINEXPLUATATION_AVG                                         48.78%\n",
      "prev_app_DOWN_PAYMENT_PNT_var                                       46.22%\n",
      "prev_app_DAYS_LAST_DUE_std                                          44.91%\n",
      "bureau_AMT_CREDIT_MAX_OVERDUE_max                                    40.2%\n",
      "prev_app_DAYS_LAST_DUE_1ST_VERSION_std                              36.34%\n",
      "prev_app_ANNUITY_CREDIT_RATIO_std                                   26.25%\n",
      "bureau_AMT_CREDIT_SUM_var                                           26.05%\n",
      "bureau_DAYS_ENDDATE_FACT_max                                        25.09%\n",
      "prev_app_DAYS_DECISION_std                                          22.43%\n",
      "bureau_AMT_CREDIT_SUM_LIMIT_max                                     21.16%\n",
      "EXT_SOURCE_3                                                        19.83%\n",
      "bureau_AMT_CREDIT_SUM_DEBT_mean                                     16.71%\n",
      "bureau_DAYS_CREDIT_ENDDATE_min                                      15.05%\n",
      "bureau_AMT_CREDIT_SUM_max                                           14.32%\n",
      "bureau_AMT_CREDIT_SUM_min                                           14.32%\n",
      "bureau_AMT_CREDIT_SUM_mean                                          14.32%\n",
      "bureau_DAYS_CREDIT_mean                                             14.31%\n",
      "bureau_CREDIT_TYPE_Car loan_sum                                     14.31%\n",
      "bureau_DAYS_CREDIT_UPDATE_max                                       14.31%\n",
      "bureau_CREDIT_TYPE_Credit card_sum                                  14.31%\n",
      "bureau_CREDIT_TYPE_Microloan_sum                                    14.31%\n",
      "bureau_CREDIT_TYPE_Consumer credit_sum                              14.31%\n",
      "bureau_CNT_CREDIT_PROLONG_max                                       14.31%\n",
      "bureau_AMT_CREDIT_SUM_OVERDUE_max                                   14.31%\n",
      "bureau_CREDIT_DAY_OVERDUE_mean                                      14.31%\n",
      "bureau_CREDIT_TYPE_Mortgage_sum                                     14.31%\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR                                           13.5%\n",
      "AMT_REQ_CREDIT_BUREAU_QRT                                            13.5%\n",
      "AMT_REQ_CREDIT_BUREAU_MON                                            13.5%\n",
      "prev_app_DAYS_LAST_DUE_max                                          12.49%\n",
      "prev_app_RATE_DOWN_PAYMENT_min                                      11.03%\n",
      "prev_app_RATE_DOWN_PAYMENT_mean                                     11.03%\n",
      "prev_app_RATE_DOWN_PAYMENT_max                                      11.03%\n",
      "prev_app_AMT_DOWN_PAYMENT_min                                       11.03%\n",
      "prev_app_AMT_DOWN_PAYMENT_max                                       11.03%\n",
      "DEF_30_CNT_SOCIAL_CIRCLE                                             0.33%\n",
      "OBS_30_CNT_SOCIAL_CIRCLE                                             0.33%\n",
      "EXT_SOURCE_2                                                         0.21%\n",
      "ORGANIZATION_TYPE_Trade: type 2                                       0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 2                                    0.0%\n",
      "ORGANIZATION_TYPE_Hotel                                               0.0%\n",
      "ORGANIZATION_TYPE_Housing                                             0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 1                                    0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 10                                   0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 11                                   0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 12                                   0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 13                                   0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 6                                    0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 3                                    0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 4                                    0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 5                                    0.0%\n",
      "ORGANIZATION_TYPE_Emergency                                           0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 7                                    0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 8                                    0.0%\n",
      "ORGANIZATION_TYPE_Industry: type 9                                    0.0%\n",
      "ORGANIZATION_TYPE_Insurance                                           0.0%\n",
      "ORGANIZATION_TYPE_Government                                          0.0%\n",
      "ORGANIZATION_TYPE_Cleaning                                            0.0%\n",
      "ORGANIZATION_TYPE_Electricity                                         0.0%\n",
      "ORGANIZATION_TYPE_Culture                                             0.0%\n",
      "WEEKDAY_APPR_PROCESS_START_FRIDAY                                     0.0%\n",
      "WEEKDAY_APPR_PROCESS_START_MONDAY                                     0.0%\n",
      "WEEKDAY_APPR_PROCESS_START_SATURDAY                                   0.0%\n",
      "WEEKDAY_APPR_PROCESS_START_SUNDAY                                     0.0%\n",
      "WEEKDAY_APPR_PROCESS_START_THURSDAY                                   0.0%\n",
      "WEEKDAY_APPR_PROCESS_START_TUESDAY                                    0.0%\n",
      "WEEKDAY_APPR_PROCESS_START_WEDNESDAY                                  0.0%\n",
      "ORGANIZATION_TYPE_Advertising                                         0.0%\n",
      "ORGANIZATION_TYPE_Agriculture                                         0.0%\n",
      "ORGANIZATION_TYPE_Bank                                                0.0%\n",
      "ORGANIZATION_TYPE_Business Entity Type 1                              0.0%\n",
      "ORGANIZATION_TYPE_Business Entity Type 2                              0.0%\n",
      "ORGANIZATION_TYPE_Business Entity Type 3                              0.0%\n",
      "ORGANIZATION_TYPE_Legal Services                                      0.0%\n",
      "ORGANIZATION_TYPE_Construction                                        0.0%\n",
      "ORGANIZATION_TYPE_Kindergarten                                        0.0%\n",
      "ORGANIZATION_TYPE_Other                                               0.0%\n",
      "ORGANIZATION_TYPE_Medicine                                            0.0%\n",
      "WALLSMATERIAL_MODE_Monolithic                                         0.0%\n",
      "WALLSMATERIAL_MODE_Block                                              0.0%\n",
      "FONDKAPREMONT_MODE_reg oper spec account                              0.0%\n",
      "FONDKAPREMONT_MODE_reg oper account                                   0.0%\n",
      "FONDKAPREMONT_MODE_org spec account                                   0.0%\n",
      "FONDKAPREMONT_MODE_not specified                                      0.0%\n",
      "ORGANIZATION_TYPE_University                                          0.0%\n",
      "ORGANIZATION_TYPE_Transport: type 4                                   0.0%\n",
      "ORGANIZATION_TYPE_Transport: type 3                                   0.0%\n",
      "ORGANIZATION_TYPE_Transport: type 2                                   0.0%\n",
      "ORGANIZATION_TYPE_Transport: type 1                                   0.0%\n",
      "ORGANIZATION_TYPE_Trade: type 7                                       0.0%\n",
      "ORGANIZATION_TYPE_Trade: type 6                                       0.0%\n",
      "ORGANIZATION_TYPE_Trade: type 5                                       0.0%\n",
      "ORGANIZATION_TYPE_Trade: type 4                                       0.0%\n",
      "ORGANIZATION_TYPE_Trade: type 3                                       0.0%\n",
      "WALLSMATERIAL_MODE_Mixed                                              0.0%\n",
      "WALLSMATERIAL_MODE_Others                                             0.0%\n",
      "ORGANIZATION_TYPE_Military                                            0.0%\n",
      "WALLSMATERIAL_MODE_Panel                                              0.0%\n",
      "ORGANIZATION_TYPE_Mobile                                              0.0%\n",
      "OCCUPATION_TYPE_Security staff                                        0.0%\n",
      "ORGANIZATION_TYPE_Police                                              0.0%\n",
      "ORGANIZATION_TYPE_Postal                                              0.0%\n",
      "ORGANIZATION_TYPE_Realtor                                             0.0%\n",
      "ORGANIZATION_TYPE_Religion                                            0.0%\n",
      "ORGANIZATION_TYPE_Restaurant                                          0.0%\n",
      "ORGANIZATION_TYPE_School                                              0.0%\n",
      "ORGANIZATION_TYPE_Security                                            0.0%\n",
      "ORGANIZATION_TYPE_Security Ministries                                 0.0%\n",
      "ORGANIZATION_TYPE_Self-employed                                       0.0%\n",
      "ORGANIZATION_TYPE_Services                                            0.0%\n",
      "ORGANIZATION_TYPE_Telecom                                             0.0%\n",
      "ORGANIZATION_TYPE_Trade: type 1                                       0.0%\n",
      "WALLSMATERIAL_MODE_Stone, brick                                       0.0%\n",
      "OCCUPATION_TYPE_Waiters/barmen staff                                  0.0%\n",
      "SK_ID_CURR                                                            0.0%\n",
      "OCCUPATION_TYPE_Secretaries                                           0.0%\n",
      "FLAG_OWN_CAR_Y                                                        0.0%\n",
      "DAYS_LAST_PHONE_CHANGE                                                0.0%\n",
      "TARGET                                                                0.0%\n",
      "NAME_CONTRACT_TYPE_Cash loans                                         0.0%\n",
      "NAME_CONTRACT_TYPE_Revolving loans                                    0.0%\n",
      "CODE_GENDER_F                                                         0.0%\n",
      "CODE_GENDER_M                                                         0.0%\n",
      "FLAG_OWN_CAR_N                                                        0.0%\n",
      "FLAG_OWN_REALTY_N                                                     0.0%\n",
      "OCCUPATION_TYPE_Sales staff                                           0.0%\n",
      "FLAG_OWN_REALTY_Y                                                     0.0%\n",
      "NAME_TYPE_SUITE_Children                                              0.0%\n",
      "NAME_TYPE_SUITE_Family                                                0.0%\n",
      "NAME_TYPE_SUITE_Group of people                                       0.0%\n",
      "NAME_TYPE_SUITE_Other_A                                               0.0%\n",
      "NAME_TYPE_SUITE_Other_B                                               0.0%\n",
      "NAME_TYPE_SUITE_Spouse, partner                                       0.0%\n",
      "LIVE_CITY_NOT_WORK_CITY                                               0.0%\n",
      "REG_CITY_NOT_WORK_CITY                                                0.0%\n",
      "REG_CITY_NOT_LIVE_CITY                                                0.0%\n",
      "REG_REGION_NOT_WORK_REGION                                            0.0%\n",
      "CNT_CHILDREN                                                          0.0%\n",
      "AMT_INCOME_TOTAL                                                      0.0%\n",
      "AMT_CREDIT                                                            0.0%\n",
      "AMT_ANNUITY                                                           0.0%\n",
      "REGION_POPULATION_RELATIVE                                            0.0%\n",
      "DAYS_BIRTH                                                            0.0%\n",
      "DAYS_EMPLOYED                                                         0.0%\n",
      "DAYS_REGISTRATION                                                     0.0%\n",
      "DAYS_ID_PUBLISH                                                       0.0%\n",
      "FLAG_EMP_PHONE                                                        0.0%\n",
      "FLAG_WORK_PHONE                                                       0.0%\n",
      "FLAG_PHONE                                                            0.0%\n",
      "FLAG_EMAIL                                                            0.0%\n",
      "REGION_RATING_CLIENT                                                  0.0%\n",
      "HOUR_APPR_PROCESS_START                                               0.0%\n",
      "NAME_TYPE_SUITE_Unaccompanied                                         0.0%\n",
      "NAME_INCOME_TYPE_Businessman                                          0.0%\n",
      "NAME_INCOME_TYPE_Commercial associate                                 0.0%\n",
      "NAME_HOUSING_TYPE_Office apartment                                    0.0%\n",
      "NAME_HOUSING_TYPE_With parents                                        0.0%\n",
      "OCCUPATION_TYPE_Accountants                                           0.0%\n",
      "OCCUPATION_TYPE_Cleaning staff                                        0.0%\n",
      "OCCUPATION_TYPE_Cooking staff                                         0.0%\n",
      "OCCUPATION_TYPE_Core staff                                            0.0%\n",
      "OCCUPATION_TYPE_Drivers                                               0.0%\n",
      "OCCUPATION_TYPE_HR staff                                              0.0%\n",
      "OCCUPATION_TYPE_High skill tech staff                                 0.0%\n",
      "OCCUPATION_TYPE_IT staff                                              0.0%\n",
      "OCCUPATION_TYPE_Laborers                                              0.0%\n",
      "OCCUPATION_TYPE_Low-skill Laborers                                    0.0%\n",
      "OCCUPATION_TYPE_Managers                                              0.0%\n",
      "OCCUPATION_TYPE_Medicine staff                                        0.0%\n",
      "OCCUPATION_TYPE_Private service staff                                 0.0%\n",
      "OCCUPATION_TYPE_Realty agents                                         0.0%\n",
      "NAME_HOUSING_TYPE_Rented apartment                                    0.0%\n",
      "NAME_HOUSING_TYPE_Municipal apartment                                 0.0%\n",
      "NAME_INCOME_TYPE_Maternity leave                                      0.0%\n",
      "NAME_HOUSING_TYPE_House / apartment                                   0.0%\n",
      "NAME_INCOME_TYPE_State servant                                        0.0%\n",
      "NAME_INCOME_TYPE_Student                                              0.0%\n",
      "NAME_INCOME_TYPE_Unemployed                                           0.0%\n",
      "NAME_INCOME_TYPE_Working                                              0.0%\n",
      "NAME_EDUCATION_TYPE_Academic degree                                   0.0%\n",
      "NAME_EDUCATION_TYPE_Higher education                                  0.0%\n",
      "NAME_EDUCATION_TYPE_Incomplete higher                                 0.0%\n",
      "NAME_EDUCATION_TYPE_Lower secondary                                   0.0%\n",
      "NAME_EDUCATION_TYPE_Secondary / secondary special                     0.0%\n",
      "NAME_FAMILY_STATUS_Civil marriage                                     0.0%\n",
      "NAME_FAMILY_STATUS_Married                                            0.0%\n",
      "NAME_FAMILY_STATUS_Separated                                          0.0%\n",
      "NAME_FAMILY_STATUS_Single / not married                               0.0%\n",
      "NAME_FAMILY_STATUS_Widow                                              0.0%\n",
      "NAME_HOUSING_TYPE_Co-op apartment                                     0.0%\n",
      "WALLSMATERIAL_MODE_Wooden                                             0.0%\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "show_missing_values(raw_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_label = raw_data['TARGET']\n",
    "raw_predictors = raw_data.drop(['SK_ID_CURR', 'TARGET'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24825\n"
     ]
    }
   ],
   "source": [
    "print(len(raw_data[raw_data['TARGET'] == 1]))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature importance using a base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x_train: (215253, 334)\n",
      "Shape of y_train: (215253,)\n",
      "Shape of x_test: (92252, 334)\n",
      "Shape of y_test: (92252,)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(raw_predictors, raw_label, test_size = 0.3, random_state=42)\n",
    "\n",
    "print('Shape of x_train: ' + str(x_train.shape))\n",
    "print('Shape of y_train: ' + str(y_train.shape))\n",
    "print('Shape of x_test: ' + str(x_test.shape))\n",
    "print('Shape of y_test: ' + str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.386908358509567\n",
      "11.39793802557309\n",
      "11.361248827549243\n"
     ]
    }
   ],
   "source": [
    "print((len(raw_data)-sum(raw_data['TARGET'].values))/sum(raw_data['TARGET'].values))\n",
    "\n",
    "print((len(y_train)-sum(y_train))/sum(y_train))\n",
    "\n",
    "print((len(y_test)-sum(y_test))/sum(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMT_CREDIT\n",
      "AMT_ANNUITY\n",
      "DAYS_BIRTH\n",
      "DAYS_EMPLOYED\n",
      "DAYS_ID_PUBLISH\n",
      "OWN_CAR_AGE\n",
      "FLAG_EMP_PHONE\n",
      "FLAG_WORK_PHONE\n",
      "REGION_RATING_CLIENT\n",
      "REG_REGION_NOT_WORK_REGION\n",
      "REG_CITY_NOT_LIVE_CITY\n",
      "EXT_SOURCE_1\n",
      "EXT_SOURCE_2\n",
      "EXT_SOURCE_3\n",
      "FLOORSMAX_AVG\n",
      "NONLIVINGAPARTMENTS_AVG\n",
      "DEF_30_CNT_SOCIAL_CIRCLE\n",
      "AMT_REQ_CREDIT_BUREAU_QRT\n",
      "bureau_DAYS_CREDIT_mean\n",
      "bureau_AMT_CREDIT_MAX_OVERDUE_max\n",
      "bureau_AMT_CREDIT_SUM_mean\n",
      "bureau_AMT_CREDIT_SUM_var\n",
      "bureau_AMT_CREDIT_SUM_DEBT_mean\n",
      "bureau_AMT_CREDIT_SUM_LIMIT_max\n",
      "bureau_AMT_CREDIT_SUM_OVERDUE_max\n",
      "bureau_CREDIT_TYPE_Microloan_sum\n",
      "bureau_CREDIT_TYPE_Mortgage_sum\n",
      "credit_card_MONTHS_BALANCE_mean\n",
      "credit_card_AMT_BALANCE_max\n",
      "credit_card_AMT_BALANCE_sum\n",
      "credit_card_AMT_DRAWINGS_ATM_CURRENT_max\n",
      "credit_card_AMT_DRAWINGS_ATM_CURRENT_mean\n",
      "credit_card_AMT_DRAWINGS_CURRENT_sum\n",
      "credit_card_AMT_DRAWINGS_OTHER_CURRENT_max\n",
      "credit_card_AMT_DRAWINGS_POS_CURRENT_sum\n",
      "credit_card_AMT_PAYMENT_CURRENT_max\n",
      "credit_card_AMT_PAYMENT_CURRENT_median\n",
      "credit_card_CNT_DRAWINGS_ATM_CURRENT_mean\n",
      "credit_card_CNT_DRAWINGS_CURRENT_max\n",
      "credit_card_CNT_DRAWINGS_CURRENT_mean\n",
      "credit_card_SK_DPD_DEF_max\n",
      "credit_card_PNT_BALANCE_DRAWINGS_ATM_median\n",
      "credit_card_AVG_DRAWINGS_POS_CURRENT_max\n",
      "credit_card_AVG_DRAWINGS_POS_CURRENT_mean\n",
      "credit_card_AVG_DRAWINGS_POS_CURRENT_sum\n",
      "cash_balance_SK_DPD_mean\n",
      "cash_balance_SK_DPD_DEF_mean\n",
      "prev_app_AMT_APPLICATION_max\n",
      "prev_app_AMT_DOWN_PAYMENT_max\n",
      "prev_app_AMT_GOODS_PRICE_min\n",
      "prev_app_RATE_DOWN_PAYMENT_max\n",
      "prev_app_DAYS_DECISION_min\n",
      "prev_app_CNT_PAYMENT_max\n",
      "prev_app_CNT_PAYMENT_mean\n",
      "prev_app_DAYS_LAST_DUE_1ST_VERSION_max\n",
      "prev_app_DAYS_LAST_DUE_1ST_VERSION_std\n",
      "prev_app_DAYS_LAST_DUE_max\n",
      "prev_app_DAYS_LAST_DUE_std\n",
      "prev_app_ANNUITY_CREDIT_RATIO_min\n",
      "prev_app_NAME_CASH_LOAN_PURPOSE_Medicine_sum\n",
      "prev_app_NAME_CASH_LOAN_PURPOSE_Repairs_sum\n",
      "prev_app_CODE_REJECT_REASON_HC_sum\n",
      "prev_app_CODE_REJECT_REASON_LIMIT_sum\n",
      "prev_app_CODE_REJECT_REASON_SCOFR_sum\n",
      "prev_app_NAME_CLIENT_TYPE_New_sum\n",
      "prev_app_NAME_GOODS_CATEGORY_Furniture_sum\n",
      "prev_app_NAME_PORTFOLIO_Cards_sum\n",
      "prev_app_NAME_PRODUCT_TYPE_walk-in_sum\n",
      "prev_app_CHANNEL_TYPE_AP+ (Cash loan)_sum\n",
      "prev_app_CHANNEL_TYPE_Channel of corporate sales_sum\n",
      "prev_app_NAME_SELLER_INDUSTRY_Construction_sum\n",
      "prev_app_NAME_YIELD_GROUP_high_sum\n",
      "prev_app_NAME_YIELD_GROUP_low_action_sum\n",
      "prev_app_NAME_YIELD_GROUP_low_normal_sum\n",
      "install_payments_AMT_INSTALMENT_mean\n",
      "NAME_CONTRACT_TYPE_Cash loans\n",
      "CODE_GENDER_F\n",
      "FLAG_OWN_CAR_N\n",
      "NAME_TYPE_SUITE_Other_B\n",
      "NAME_INCOME_TYPE_State servant\n",
      "NAME_INCOME_TYPE_Working\n",
      "NAME_EDUCATION_TYPE_Higher education\n",
      "NAME_EDUCATION_TYPE_Secondary / secondary special\n",
      "NAME_FAMILY_STATUS_Civil marriage\n",
      "NAME_FAMILY_STATUS_Married\n",
      "OCCUPATION_TYPE_Core staff\n",
      "OCCUPATION_TYPE_Drivers\n",
      "OCCUPATION_TYPE_Laborers\n",
      "OCCUPATION_TYPE_Managers\n",
      "ORGANIZATION_TYPE_Construction\n",
      "ORGANIZATION_TYPE_Industry: type 3\n",
      "ORGANIZATION_TYPE_Industry: type 4\n",
      "ORGANIZATION_TYPE_Military\n",
      "ORGANIZATION_TYPE_Police\n",
      "ORGANIZATION_TYPE_Self-employed\n",
      "ORGANIZATION_TYPE_Transport: type 2\n",
      "ORGANIZATION_TYPE_Transport: type 3\n",
      "ORGANIZATION_TYPE_Transport: type 4\n",
      "FONDKAPREMONT_MODE_not specified\n",
      "WALLSMATERIAL_MODE_Stone, brick\n"
     ]
    }
   ],
   "source": [
    "#Filter the inital 250 variables to 100 using XGBoostClassifier and selecting the top 100 features by feature importance\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "#Define XGBoost parameters tree_method = 'gpu_hist'\n",
    "xgboost = XGBClassifier(learning_rate = 0.3,random_state=0,use_label_encoder=False)\n",
    "\n",
    "#Get top 100 features using XGBoost\n",
    "select_feat = SelectFromModel(xgboost,threshold=-np.inf,max_features=100).fit(x_train,y_train)\n",
    "\n",
    "#Transform the training and test sets to only include the top 100 features\n",
    "x_train_feat = select_feat.transform(x_train)\n",
    "x_test_feat = select_feat.transform(x_test)\n",
    "\n",
    "#Get indices of top 100 features \n",
    "feat_index = select_feat.get_support()\n",
    "\n",
    "#Rename the columns of training and test sets to include column names of top 100 features\n",
    "train_x = pd.DataFrame(x_train, columns = x_train.columns[feat_index])\n",
    "test_x = pd.DataFrame(x_test, columns= x_test.columns[feat_index])\n",
    "\n",
    "\n",
    "#Print the top 100 features\n",
    "for col in x_train.columns[feat_index]:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x_columns = pd.DataFrame(columns = train_x.columns)\n",
    "\n",
    "train_x_columns.to_csv(\"XGBoost_model_columns.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# XGBoost hyperparameter grid\n",
    "params = {\n",
    "        'colsample_bytree': [0.5,1],\n",
    "        'colsample_bylevel': [0.5,1],\n",
    "        'colsample_bynode': [0.5,1],\n",
    "        'learning_rate': [0.01, 0.1, 0.2],\n",
    "        'min_child_weight': [1, 5, 10],\n",
    "        'subsample': [0.25, 0.5, 1.0],\n",
    "        'max_depth': [3, 5, 7, 9],\n",
    "        'scale_pos_weight': [1, 5, 10], \n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define XGBoost base model \n",
    "xgb = XGBClassifier(n_estimators=100, objective='binary:logistic', \n",
    "                    #gpu_id = 0, tree_method = 'gpu_hist',\n",
    "                    random_state=42,use_label_encoder=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import time\n",
    "\n",
    "# #Apply grid search with 5-fold cross validation and scoring metric of roc_auc\n",
    "# start_time = time.time()\n",
    "\n",
    "# grid_search = GridSearchCV(xgb, params, scoring='roc_auc', cv=5,verbose=3)\n",
    "# grid_search.fit(train_x, y_train)\n",
    "\n",
    "# end_time = time.time()\n",
    "# print(end_time - start_time)\n",
    "\n",
    "# #Get results of gridsearch \n",
    "# grid_results_df = pd.DataFrame(grid_search.cv_results_)\n",
    "# #Save to csv\n",
    "# grid_results_df.to_csv(\"XGBoost_CV_results.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_colsample_bylevel</th>\n",
       "      <th>param_colsample_bynode</th>\n",
       "      <th>param_colsample_bytree</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_min_child_weight</th>\n",
       "      <th>param_scale_pos_weight</th>\n",
       "      <th>param_subsample</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>9.263226</td>\n",
       "      <td>0.057725</td>\n",
       "      <td>0.059454</td>\n",
       "      <td>0.001498</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{'colsample_bylevel': 1, 'colsample_bynode': 0...</td>\n",
       "      <td>0.780575</td>\n",
       "      <td>0.773389</td>\n",
       "      <td>0.777255</td>\n",
       "      <td>0.766353</td>\n",
       "      <td>0.775306</td>\n",
       "      <td>0.774576</td>\n",
       "      <td>0.004748</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>10.796691</td>\n",
       "      <td>0.362525</td>\n",
       "      <td>0.078672</td>\n",
       "      <td>0.014975</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{'colsample_bylevel': 0.5, 'colsample_bynode':...</td>\n",
       "      <td>0.780575</td>\n",
       "      <td>0.773389</td>\n",
       "      <td>0.777255</td>\n",
       "      <td>0.766353</td>\n",
       "      <td>0.775306</td>\n",
       "      <td>0.774576</td>\n",
       "      <td>0.004748</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2117</th>\n",
       "      <td>14.129648</td>\n",
       "      <td>0.480600</td>\n",
       "      <td>0.064459</td>\n",
       "      <td>0.009208</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{'colsample_bylevel': 1, 'colsample_bynode': 1...</td>\n",
       "      <td>0.779686</td>\n",
       "      <td>0.772797</td>\n",
       "      <td>0.777770</td>\n",
       "      <td>0.766240</td>\n",
       "      <td>0.772648</td>\n",
       "      <td>0.773828</td>\n",
       "      <td>0.004688</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2126</th>\n",
       "      <td>13.528101</td>\n",
       "      <td>0.056713</td>\n",
       "      <td>0.057452</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{'colsample_bylevel': 1, 'colsample_bynode': 1...</td>\n",
       "      <td>0.779317</td>\n",
       "      <td>0.772366</td>\n",
       "      <td>0.777067</td>\n",
       "      <td>0.766968</td>\n",
       "      <td>0.772828</td>\n",
       "      <td>0.773709</td>\n",
       "      <td>0.004260</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>9.494137</td>\n",
       "      <td>0.150151</td>\n",
       "      <td>0.059254</td>\n",
       "      <td>0.001941</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{'colsample_bylevel': 1, 'colsample_bynode': 0...</td>\n",
       "      <td>0.778435</td>\n",
       "      <td>0.772989</td>\n",
       "      <td>0.778147</td>\n",
       "      <td>0.764445</td>\n",
       "      <td>0.773973</td>\n",
       "      <td>0.773598</td>\n",
       "      <td>0.005067</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "1478       9.263226      0.057725         0.059454        0.001498   \n",
       "830       10.796691      0.362525         0.078672        0.014975   \n",
       "2117      14.129648      0.480600         0.064459        0.009208   \n",
       "2126      13.528101      0.056713         0.057452        0.000490   \n",
       "1460       9.494137      0.150151         0.059254        0.001941   \n",
       "\n",
       "      param_colsample_bylevel  param_colsample_bynode  param_colsample_bytree  \\\n",
       "1478                      1.0                     0.5                     0.5   \n",
       "830                       0.5                     1.0                     0.5   \n",
       "2117                      1.0                     1.0                     0.5   \n",
       "2126                      1.0                     1.0                     0.5   \n",
       "1460                      1.0                     0.5                     0.5   \n",
       "\n",
       "      param_learning_rate  param_max_depth  param_min_child_weight  \\\n",
       "1478                  0.1                7                      10   \n",
       "830                   0.1                7                      10   \n",
       "2117                  0.1                7                       5   \n",
       "2126                  0.1                7                      10   \n",
       "1460                  0.1                7                       1   \n",
       "\n",
       "      param_scale_pos_weight  param_subsample  \\\n",
       "1478                       1              1.0   \n",
       "830                        1              1.0   \n",
       "2117                       1              1.0   \n",
       "2126                       1              1.0   \n",
       "1460                       1              1.0   \n",
       "\n",
       "                                                 params  split0_test_score  \\\n",
       "1478  {'colsample_bylevel': 1, 'colsample_bynode': 0...           0.780575   \n",
       "830   {'colsample_bylevel': 0.5, 'colsample_bynode':...           0.780575   \n",
       "2117  {'colsample_bylevel': 1, 'colsample_bynode': 1...           0.779686   \n",
       "2126  {'colsample_bylevel': 1, 'colsample_bynode': 1...           0.779317   \n",
       "1460  {'colsample_bylevel': 1, 'colsample_bynode': 0...           0.778435   \n",
       "\n",
       "      split1_test_score  split2_test_score  split3_test_score  \\\n",
       "1478           0.773389           0.777255           0.766353   \n",
       "830            0.773389           0.777255           0.766353   \n",
       "2117           0.772797           0.777770           0.766240   \n",
       "2126           0.772366           0.777067           0.766968   \n",
       "1460           0.772989           0.778147           0.764445   \n",
       "\n",
       "      split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "1478           0.775306         0.774576        0.004748                1  \n",
       "830            0.775306         0.774576        0.004748                1  \n",
       "2117           0.772648         0.773828        0.004688                3  \n",
       "2126           0.772828         0.773709        0.004260                4  \n",
       "1460           0.773973         0.773598        0.005067                5  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_results_df = pd.read_csv(os.getcwd()+\"\\\\XGBoost_CV_results.csv\")\n",
    "\n",
    "#Print out the results of the top 5 models from the gridsearch\n",
    "grid_results_df.sort_values(by=['rank_test_score']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Print out the best XGBoost parameters from the gridsearch\n",
    "# print('The best XGBoost parameters from the grid search were:')\n",
    "# print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=0.5, colsample_bytree=0.5,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.1, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=7, max_leaves=0, min_child_weight=10,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_ = XGBClassifier(n_estimators = 100, objective = 'binary:logistic', \n",
    "                     colsample_bylevel = 1, colsample_bytree = 0.5,colsample_bynode = 0.5, \n",
    "                     learning_rate = 0.1, max_depth = 7, min_child_weight = 10, scale_pos_weight = 1, subsample = 1)\n",
    "\n",
    "best_model = xgb_.fit(train_x, y_train)\n",
    "best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------Train Set Evaluation -----------------\n",
      "Accuracy Score:  0.9231 \n",
      "\n",
      "Area under ROC Curve:  0.8389 \n",
      "\n",
      "Average Precision Score:  0.4011 \n",
      "\n",
      "Confusion Matrix:\n",
      "                Positive Prediction  Negative Prediction\n",
      "Positive Class               197672                  219\n",
      "Negative Class                16344                 1018\n",
      "\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      1.00      0.96    197891\n",
      "           1       0.82      0.06      0.11     17362\n",
      "\n",
      "    accuracy                           0.92    215253\n",
      "   macro avg       0.87      0.53      0.53    215253\n",
      "weighted avg       0.92      0.92      0.89    215253\n",
      "\n"
     ]
    }
   ],
   "source": [
    "show_evaluation(train_x, y_train, best_model, train = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------Test Set Evaluation -----------------\n",
      "Accuracy Score:  0.9197 \n",
      "\n",
      "Area under ROC Curve:  0.7738 \n",
      "\n",
      "Average Precision Score:  0.2654 \n",
      "\n",
      "Confusion Matrix:\n",
      "                Positive Prediction  Negative Prediction\n",
      "Positive Class                84604                  185\n",
      "Negative Class                 7226                  237\n",
      "\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      1.00      0.96     84789\n",
      "           1       0.56      0.03      0.06      7463\n",
      "\n",
      "    accuracy                           0.92     92252\n",
      "   macro avg       0.74      0.51      0.51     92252\n",
      "weighted avg       0.89      0.92      0.89     92252\n",
      "\n"
     ]
    }
   ],
   "source": [
    "show_evaluation(test_x, y_test, best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AMT_CREDIT': 251.0,\n",
       " 'AMT_ANNUITY': 184.0,\n",
       " 'DAYS_BIRTH': 236.0,\n",
       " 'DAYS_EMPLOYED': 232.0,\n",
       " 'DAYS_ID_PUBLISH': 151.0,\n",
       " 'OWN_CAR_AGE': 83.0,\n",
       " 'FLAG_EMP_PHONE': 8.0,\n",
       " 'FLAG_WORK_PHONE': 20.0,\n",
       " 'REGION_RATING_CLIENT': 75.0,\n",
       " 'REG_REGION_NOT_WORK_REGION': 3.0,\n",
       " 'REG_CITY_NOT_LIVE_CITY': 23.0,\n",
       " 'EXT_SOURCE_1': 324.0,\n",
       " 'EXT_SOURCE_2': 505.0,\n",
       " 'EXT_SOURCE_3': 405.0,\n",
       " 'FLOORSMAX_AVG': 92.0,\n",
       " 'NONLIVINGAPARTMENTS_AVG': 26.0,\n",
       " 'DEF_30_CNT_SOCIAL_CIRCLE': 39.0,\n",
       " 'AMT_REQ_CREDIT_BUREAU_QRT': 18.0,\n",
       " 'bureau_DAYS_CREDIT_mean': 233.0,\n",
       " 'bureau_AMT_CREDIT_MAX_OVERDUE_max': 191.0,\n",
       " 'bureau_AMT_CREDIT_SUM_mean': 164.0,\n",
       " 'bureau_AMT_CREDIT_SUM_var': 236.0,\n",
       " 'bureau_AMT_CREDIT_SUM_DEBT_mean': 221.0,\n",
       " 'bureau_AMT_CREDIT_SUM_LIMIT_max': 83.0,\n",
       " 'bureau_AMT_CREDIT_SUM_OVERDUE_max': 31.0,\n",
       " 'bureau_CREDIT_TYPE_Microloan_sum': 47.0,\n",
       " 'bureau_CREDIT_TYPE_Mortgage_sum': 13.0,\n",
       " 'credit_card_MONTHS_BALANCE_mean': 37.0,\n",
       " 'credit_card_AMT_BALANCE_max': 97.0,\n",
       " 'credit_card_AMT_BALANCE_sum': 82.0,\n",
       " 'credit_card_AMT_DRAWINGS_ATM_CURRENT_max': 47.0,\n",
       " 'credit_card_AMT_DRAWINGS_ATM_CURRENT_mean': 80.0,\n",
       " 'credit_card_AMT_DRAWINGS_CURRENT_sum': 34.0,\n",
       " 'credit_card_AMT_DRAWINGS_OTHER_CURRENT_max': 19.0,\n",
       " 'credit_card_AMT_DRAWINGS_POS_CURRENT_sum': 31.0,\n",
       " 'credit_card_AMT_PAYMENT_CURRENT_max': 81.0,\n",
       " 'credit_card_AMT_PAYMENT_CURRENT_median': 53.0,\n",
       " 'credit_card_CNT_DRAWINGS_ATM_CURRENT_mean': 143.0,\n",
       " 'credit_card_CNT_DRAWINGS_CURRENT_max': 61.0,\n",
       " 'credit_card_CNT_DRAWINGS_CURRENT_mean': 107.0,\n",
       " 'credit_card_SK_DPD_DEF_max': 8.0,\n",
       " 'credit_card_PNT_BALANCE_DRAWINGS_ATM_median': 26.0,\n",
       " 'credit_card_AVG_DRAWINGS_POS_CURRENT_max': 54.0,\n",
       " 'credit_card_AVG_DRAWINGS_POS_CURRENT_mean': 70.0,\n",
       " 'credit_card_AVG_DRAWINGS_POS_CURRENT_sum': 36.0,\n",
       " 'cash_balance_SK_DPD_mean': 118.0,\n",
       " 'cash_balance_SK_DPD_DEF_mean': 143.0,\n",
       " 'prev_app_AMT_APPLICATION_max': 156.0,\n",
       " 'prev_app_AMT_DOWN_PAYMENT_max': 159.0,\n",
       " 'prev_app_AMT_GOODS_PRICE_min': 138.0,\n",
       " 'prev_app_RATE_DOWN_PAYMENT_max': 160.0,\n",
       " 'prev_app_DAYS_DECISION_min': 235.0,\n",
       " 'prev_app_CNT_PAYMENT_max': 97.0,\n",
       " 'prev_app_CNT_PAYMENT_mean': 207.0,\n",
       " 'prev_app_DAYS_LAST_DUE_1ST_VERSION_max': 281.0,\n",
       " 'prev_app_DAYS_LAST_DUE_1ST_VERSION_std': 208.0,\n",
       " 'prev_app_DAYS_LAST_DUE_max': 189.0,\n",
       " 'prev_app_DAYS_LAST_DUE_std': 204.0,\n",
       " 'prev_app_ANNUITY_CREDIT_RATIO_min': 116.0,\n",
       " 'prev_app_NAME_CASH_LOAN_PURPOSE_Medicine_sum': 2.0,\n",
       " 'prev_app_NAME_CASH_LOAN_PURPOSE_Repairs_sum': 11.0,\n",
       " 'prev_app_CODE_REJECT_REASON_HC_sum': 68.0,\n",
       " 'prev_app_CODE_REJECT_REASON_LIMIT_sum': 43.0,\n",
       " 'prev_app_CODE_REJECT_REASON_SCOFR_sum': 36.0,\n",
       " 'prev_app_NAME_CLIENT_TYPE_New_sum': 18.0,\n",
       " 'prev_app_NAME_GOODS_CATEGORY_Furniture_sum': 16.0,\n",
       " 'prev_app_NAME_PORTFOLIO_Cards_sum': 17.0,\n",
       " 'prev_app_NAME_PRODUCT_TYPE_walk-in_sum': 44.0,\n",
       " 'prev_app_CHANNEL_TYPE_AP+ (Cash loan)_sum': 21.0,\n",
       " 'prev_app_CHANNEL_TYPE_Channel of corporate sales_sum': 7.0,\n",
       " 'prev_app_NAME_SELLER_INDUSTRY_Construction_sum': 3.0,\n",
       " 'prev_app_NAME_YIELD_GROUP_high_sum': 80.0,\n",
       " 'prev_app_NAME_YIELD_GROUP_low_action_sum': 35.0,\n",
       " 'prev_app_NAME_YIELD_GROUP_low_normal_sum': 42.0,\n",
       " 'install_payments_AMT_INSTALMENT_mean': 259.0,\n",
       " 'NAME_CONTRACT_TYPE_Cash loans': 33.0,\n",
       " 'CODE_GENDER_F': 83.0,\n",
       " 'FLAG_OWN_CAR_N': 22.0,\n",
       " 'NAME_INCOME_TYPE_State servant': 4.0,\n",
       " 'NAME_INCOME_TYPE_Working': 29.0,\n",
       " 'NAME_EDUCATION_TYPE_Higher education': 56.0,\n",
       " 'NAME_EDUCATION_TYPE_Secondary / secondary special': 36.0,\n",
       " 'NAME_FAMILY_STATUS_Civil marriage': 7.0,\n",
       " 'NAME_FAMILY_STATUS_Married': 37.0,\n",
       " 'OCCUPATION_TYPE_Core staff': 9.0,\n",
       " 'OCCUPATION_TYPE_Drivers': 27.0,\n",
       " 'OCCUPATION_TYPE_Laborers': 23.0,\n",
       " 'OCCUPATION_TYPE_Managers': 3.0,\n",
       " 'ORGANIZATION_TYPE_Construction': 7.0,\n",
       " 'ORGANIZATION_TYPE_Industry: type 3': 1.0,\n",
       " 'ORGANIZATION_TYPE_Military': 5.0,\n",
       " 'ORGANIZATION_TYPE_Self-employed': 11.0,\n",
       " 'ORGANIZATION_TYPE_Transport: type 3': 4.0,\n",
       " 'ORGANIZATION_TYPE_Transport: type 4': 2.0,\n",
       " 'WALLSMATERIAL_MODE_Stone, brick': 7.0}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.get_booster().get_score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 5000x5000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_importance\n",
    "plt.figure(figsize=(50,50))\n",
    "plot_importance(best_model, max_num_features = 25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "\n",
    "explainer = shap.TreeExplainer(best_model)\n",
    "shap_values = explainer.shap_values(train_x)\n",
    "shap.summary_plot(shap_values, test_x, plot_type=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "filename = 'XGBoost_model.sav'\n",
    "pickle.dump(best_model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------Train Set Evaluation -----------------\n",
      "Accuracy Score:  0.9231 \n",
      "\n",
      "Area under ROC Curve:  0.8389 \n",
      "\n",
      "Average Precision Score:  0.4011 \n",
      "\n",
      "Confusion Matrix:\n",
      "                Positive Prediction  Negative Prediction\n",
      "Positive Class               197672                  219\n",
      "Negative Class                16344                 1018\n",
      "\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      1.00      0.96    197891\n",
      "           1       0.82      0.06      0.11     17362\n",
      "\n",
      "    accuracy                           0.92    215253\n",
      "   macro avg       0.87      0.53      0.53    215253\n",
      "weighted avg       0.92      0.92      0.89    215253\n",
      "\n"
     ]
    }
   ],
   "source": [
    "show_evaluation(train_x, y_train, best_model, train = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------Test Set Evaluation -----------------\n",
      "Accuracy Score:  0.9197 \n",
      "\n",
      "Area under ROC Curve:  0.7738 \n",
      "\n",
      "Average Precision Score:  0.2654 \n",
      "\n",
      "Confusion Matrix:\n",
      "                Positive Prediction  Negative Prediction\n",
      "Positive Class                84604                  185\n",
      "Negative Class                 7226                  237\n",
      "\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      1.00      0.96     84789\n",
      "           1       0.56      0.03      0.06      7463\n",
      "\n",
      "    accuracy                           0.92     92252\n",
      "   macro avg       0.74      0.51      0.51     92252\n",
      "weighted avg       0.89      0.92      0.89     92252\n",
      "\n"
     ]
    }
   ],
   "source": [
    "show_evaluation(test_x, y_test, best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_plot_metrics(train_x, test_x, y_train, y_test, best_model)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.8.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}