thesis/numerical_features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from outliers import mahalanobis_outlier_test\n",
    "from stats_tests import (\n",
    "    pearson_spearman_significance_test,\n",
    "    kendall_significance_test,\n",
    "    hotelling_t2_1samp_test,\n",
    "    hotelling_t2_2samp_test,\n",
    ")\n",
    "from utils import load_train_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_filename = \"application_train_numerical.csv\"\n",
    "test_filename = \"application_test_numerical.csv\"\n",
    "path_to_data = \"../data/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load train/test numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_application_train_numerical = load_train_test(\n",
    "    filename=train_filename,\n",
    "    path_to_train_folder=path_to_data,\n",
    ")\n",
    "df_application_test_numerical = load_train_test(\n",
    "    filename=test_filename,\n",
    "    path_to_train_folder=path_to_data,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.4 s, sys: 97.8 ms, total: 4.5 s\n",
      "Wall time: 4.67 s\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <th>...</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>ENTRANCES_AVG</th>\n",
       "      <th>ENTRANCES_MODE</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.024166</td>\n",
       "      <td>-0.012711</td>\n",
       "      <td>-0.032739</td>\n",
       "      <td>-0.044932</td>\n",
       "      <td>-0.009993</td>\n",
       "      <td>0.055218</td>\n",
       "      <td>-0.001557</td>\n",
       "      <td>-0.025031</td>\n",
       "      <td>-0.023393</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012462</td>\n",
       "      <td>-0.019172</td>\n",
       "      <td>-0.017387</td>\n",
       "      <td>-0.012817</td>\n",
       "      <td>0.032248</td>\n",
       "      <td>-0.013578</td>\n",
       "      <td>-0.043768</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>-0.013337</td>\n",
       "      <td>-0.022746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <td>-0.024166</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.039198</td>\n",
       "      <td>0.083698</td>\n",
       "      <td>-0.091138</td>\n",
       "      <td>-0.009779</td>\n",
       "      <td>-0.014902</td>\n",
       "      <td>0.013467</td>\n",
       "      <td>0.079199</td>\n",
       "      <td>0.071962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037673</td>\n",
       "      <td>0.020954</td>\n",
       "      <td>0.016342</td>\n",
       "      <td>0.052269</td>\n",
       "      <td>-0.005672</td>\n",
       "      <td>0.044931</td>\n",
       "      <td>0.119067</td>\n",
       "      <td>-0.002985</td>\n",
       "      <td>0.043729</td>\n",
       "      <td>0.041045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <td>-0.012711</td>\n",
       "      <td>0.039198</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.285189</td>\n",
       "      <td>-0.011726</td>\n",
       "      <td>0.006638</td>\n",
       "      <td>-0.001393</td>\n",
       "      <td>0.213163</td>\n",
       "      <td>0.275961</td>\n",
       "      <td>0.289317</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010276</td>\n",
       "      <td>0.170930</td>\n",
       "      <td>0.176349</td>\n",
       "      <td>0.041418</td>\n",
       "      <td>-0.009908</td>\n",
       "      <td>0.966087</td>\n",
       "      <td>0.225478</td>\n",
       "      <td>-0.005168</td>\n",
       "      <td>0.975839</td>\n",
       "      <td>0.260047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <td>-0.032739</td>\n",
       "      <td>0.083698</td>\n",
       "      <td>0.285189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012631</td>\n",
       "      <td>0.088638</td>\n",
       "      <td>-0.010795</td>\n",
       "      <td>0.129882</td>\n",
       "      <td>0.878263</td>\n",
       "      <td>0.874362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035483</td>\n",
       "      <td>0.619312</td>\n",
       "      <td>0.594829</td>\n",
       "      <td>0.088134</td>\n",
       "      <td>-0.015895</td>\n",
       "      <td>0.298187</td>\n",
       "      <td>0.626382</td>\n",
       "      <td>-0.001125</td>\n",
       "      <td>0.298221</td>\n",
       "      <td>0.689318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <td>-0.044932</td>\n",
       "      <td>-0.091138</td>\n",
       "      <td>-0.011726</td>\n",
       "      <td>-0.012631</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008405</td>\n",
       "      <td>0.023032</td>\n",
       "      <td>-0.004861</td>\n",
       "      <td>-0.020192</td>\n",
       "      <td>-0.019579</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.034457</td>\n",
       "      <td>0.004018</td>\n",
       "      <td>0.004943</td>\n",
       "      <td>-0.104332</td>\n",
       "      <td>0.016717</td>\n",
       "      <td>-0.012858</td>\n",
       "      <td>-0.016051</td>\n",
       "      <td>0.003072</td>\n",
       "      <td>-0.012975</td>\n",
       "      <td>-0.001382</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <td>-0.013578</td>\n",
       "      <td>0.044931</td>\n",
       "      <td>0.966087</td>\n",
       "      <td>0.298187</td>\n",
       "      <td>-0.012858</td>\n",
       "      <td>0.011543</td>\n",
       "      <td>-0.001707</td>\n",
       "      <td>0.210196</td>\n",
       "      <td>0.293276</td>\n",
       "      <td>0.285494</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013877</td>\n",
       "      <td>0.165729</td>\n",
       "      <td>0.155613</td>\n",
       "      <td>0.049593</td>\n",
       "      <td>-0.011667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.252893</td>\n",
       "      <td>-0.006022</td>\n",
       "      <td>0.990444</td>\n",
       "      <td>0.266349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <td>-0.043768</td>\n",
       "      <td>0.119067</td>\n",
       "      <td>0.225478</td>\n",
       "      <td>0.626382</td>\n",
       "      <td>-0.016051</td>\n",
       "      <td>0.125557</td>\n",
       "      <td>-0.007078</td>\n",
       "      <td>0.102242</td>\n",
       "      <td>0.586424</td>\n",
       "      <td>0.565649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045992</td>\n",
       "      <td>0.087642</td>\n",
       "      <td>0.062027</td>\n",
       "      <td>0.129179</td>\n",
       "      <td>-0.027371</td>\n",
       "      <td>0.252893</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.247247</td>\n",
       "      <td>0.327949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <td>0.000788</td>\n",
       "      <td>-0.002985</td>\n",
       "      <td>-0.005168</td>\n",
       "      <td>-0.001125</td>\n",
       "      <td>0.003072</td>\n",
       "      <td>0.002837</td>\n",
       "      <td>-0.003495</td>\n",
       "      <td>-0.002604</td>\n",
       "      <td>-0.003702</td>\n",
       "      <td>-0.003053</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014096</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.002057</td>\n",
       "      <td>0.013881</td>\n",
       "      <td>-0.002167</td>\n",
       "      <td>-0.006022</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006230</td>\n",
       "      <td>-0.000650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <td>-0.013337</td>\n",
       "      <td>0.043729</td>\n",
       "      <td>0.975839</td>\n",
       "      <td>0.298221</td>\n",
       "      <td>-0.012975</td>\n",
       "      <td>0.010547</td>\n",
       "      <td>-0.001914</td>\n",
       "      <td>0.211747</td>\n",
       "      <td>0.290558</td>\n",
       "      <td>0.287765</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012889</td>\n",
       "      <td>0.166998</td>\n",
       "      <td>0.159925</td>\n",
       "      <td>0.047009</td>\n",
       "      <td>-0.011062</td>\n",
       "      <td>0.990444</td>\n",
       "      <td>0.247247</td>\n",
       "      <td>-0.006230</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.265382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <td>-0.022746</td>\n",
       "      <td>0.041045</td>\n",
       "      <td>0.260047</td>\n",
       "      <td>0.689318</td>\n",
       "      <td>-0.001382</td>\n",
       "      <td>0.077490</td>\n",
       "      <td>-0.007418</td>\n",
       "      <td>0.094249</td>\n",
       "      <td>0.646774</td>\n",
       "      <td>0.647432</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021661</td>\n",
       "      <td>0.651160</td>\n",
       "      <td>0.626382</td>\n",
       "      <td>0.044507</td>\n",
       "      <td>-0.010114</td>\n",
       "      <td>0.266349</td>\n",
       "      <td>0.327949</td>\n",
       "      <td>-0.000650</td>\n",
       "      <td>0.265382</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              TARGET  HOUR_APPR_PROCESS_START  \\\n",
       "TARGET                      1.000000                -0.024166   \n",
       "HOUR_APPR_PROCESS_START    -0.024166                 1.000000   \n",
       "NONLIVINGAREA_MODE         -0.012711                 0.039198   \n",
       "LIVINGAREA_MEDI            -0.032739                 0.083698   \n",
       "DAYS_EMPLOYED              -0.044932                -0.091138   \n",
       "...                              ...                      ...   \n",
       "NONLIVINGAREA_AVG          -0.013578                 0.044931   \n",
       "FLOORSMAX_MEDI             -0.043768                 0.119067   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK  0.000788                -0.002985   \n",
       "NONLIVINGAREA_MEDI         -0.013337                 0.043729   \n",
       "BASEMENTAREA_AVG           -0.022746                 0.041045   \n",
       "\n",
       "                            NONLIVINGAREA_MODE  LIVINGAREA_MEDI  \\\n",
       "TARGET                               -0.012711        -0.032739   \n",
       "HOUR_APPR_PROCESS_START               0.039198         0.083698   \n",
       "NONLIVINGAREA_MODE                    1.000000         0.285189   \n",
       "LIVINGAREA_MEDI                       0.285189         1.000000   \n",
       "DAYS_EMPLOYED                        -0.011726        -0.012631   \n",
       "...                                        ...              ...   \n",
       "NONLIVINGAREA_AVG                     0.966087         0.298187   \n",
       "FLOORSMAX_MEDI                        0.225478         0.626382   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK           -0.005168        -0.001125   \n",
       "NONLIVINGAREA_MEDI                    0.975839         0.298221   \n",
       "BASEMENTAREA_AVG                      0.260047         0.689318   \n",
       "\n",
       "                            DAYS_EMPLOYED  YEARS_BEGINEXPLUATATION_MEDI  \\\n",
       "TARGET                          -0.044932                     -0.009993   \n",
       "HOUR_APPR_PROCESS_START         -0.091138                     -0.009779   \n",
       "NONLIVINGAREA_MODE              -0.011726                      0.006638   \n",
       "LIVINGAREA_MEDI                 -0.012631                      0.088638   \n",
       "DAYS_EMPLOYED                    1.000000                      0.008405   \n",
       "...                                   ...                           ...   \n",
       "NONLIVINGAREA_AVG               -0.012858                      0.011543   \n",
       "FLOORSMAX_MEDI                  -0.016051                      0.125557   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK       0.003072                      0.002837   \n",
       "NONLIVINGAREA_MEDI              -0.012975                      0.010547   \n",
       "BASEMENTAREA_AVG                -0.001382                      0.077490   \n",
       "\n",
       "                            DAYS_LAST_PHONE_CHANGE  NONLIVINGAPARTMENTS_MODE  \\\n",
       "TARGET                                    0.055218                 -0.001557   \n",
       "HOUR_APPR_PROCESS_START                  -0.014902                  0.013467   \n",
       "NONLIVINGAREA_MODE                       -0.001393                  0.213163   \n",
       "LIVINGAREA_MEDI                          -0.010795                  0.129882   \n",
       "DAYS_EMPLOYED                             0.023032                 -0.004861   \n",
       "...                                            ...                       ...   \n",
       "NONLIVINGAREA_AVG                        -0.001707                  0.210196   \n",
       "FLOORSMAX_MEDI                           -0.007078                  0.102242   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK               -0.003495                 -0.002604   \n",
       "NONLIVINGAREA_MEDI                       -0.001914                  0.211747   \n",
       "BASEMENTAREA_AVG                         -0.007418                  0.094249   \n",
       "\n",
       "                            LIVINGAPARTMENTS_AVG  LIVINGAPARTMENTS_MODE  ...  \\\n",
       "TARGET                                 -0.025031              -0.023393  ...   \n",
       "HOUR_APPR_PROCESS_START                 0.079199               0.071962  ...   \n",
       "NONLIVINGAREA_MODE                      0.275961               0.289317  ...   \n",
       "LIVINGAREA_MEDI                         0.878263               0.874362  ...   \n",
       "DAYS_EMPLOYED                          -0.020192              -0.019579  ...   \n",
       "...                                          ...                    ...  ...   \n",
       "NONLIVINGAREA_AVG                       0.293276               0.285494  ...   \n",
       "FLOORSMAX_MEDI                          0.586424               0.565649  ...   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK             -0.003702              -0.003053  ...   \n",
       "NONLIVINGAREA_MEDI                      0.290558               0.287765  ...   \n",
       "BASEMENTAREA_AVG                        0.646774               0.647432  ...   \n",
       "\n",
       "                            AMT_REQ_CREDIT_BUREAU_MON  ENTRANCES_AVG  \\\n",
       "TARGET                                      -0.012462      -0.019172   \n",
       "HOUR_APPR_PROCESS_START                      0.037673       0.020954   \n",
       "NONLIVINGAREA_MODE                           0.010276       0.170930   \n",
       "LIVINGAREA_MEDI                              0.035483       0.619312   \n",
       "DAYS_EMPLOYED                               -0.034457       0.004018   \n",
       "...                                               ...            ...   \n",
       "NONLIVINGAREA_AVG                            0.013877       0.165729   \n",
       "FLOORSMAX_MEDI                               0.045992       0.087642   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK                  -0.014096       0.001921   \n",
       "NONLIVINGAREA_MEDI                           0.012889       0.166998   \n",
       "BASEMENTAREA_AVG                             0.021661       0.651160   \n",
       "\n",
       "                            ENTRANCES_MODE  AMT_ANNUITY  \\\n",
       "TARGET                           -0.017387    -0.012817   \n",
       "HOUR_APPR_PROCESS_START           0.016342     0.052269   \n",
       "NONLIVINGAREA_MODE                0.176349     0.041418   \n",
       "LIVINGAREA_MEDI                   0.594829     0.088134   \n",
       "DAYS_EMPLOYED                     0.004943    -0.104332   \n",
       "...                                    ...          ...   \n",
       "NONLIVINGAREA_AVG                 0.155613     0.049593   \n",
       "FLOORSMAX_MEDI                    0.062027     0.129179   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK        0.002057     0.013881   \n",
       "NONLIVINGAREA_MEDI                0.159925     0.047009   \n",
       "BASEMENTAREA_AVG                  0.626382     0.044507   \n",
       "\n",
       "                            DEF_30_CNT_SOCIAL_CIRCLE  NONLIVINGAREA_AVG  \\\n",
       "TARGET                                      0.032248          -0.013578   \n",
       "HOUR_APPR_PROCESS_START                    -0.005672           0.044931   \n",
       "NONLIVINGAREA_MODE                         -0.009908           0.966087   \n",
       "LIVINGAREA_MEDI                            -0.015895           0.298187   \n",
       "DAYS_EMPLOYED                               0.016717          -0.012858   \n",
       "...                                              ...                ...   \n",
       "NONLIVINGAREA_AVG                          -0.011667           1.000000   \n",
       "FLOORSMAX_MEDI                             -0.027371           0.252893   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK                 -0.002167          -0.006022   \n",
       "NONLIVINGAREA_MEDI                         -0.011062           0.990444   \n",
       "BASEMENTAREA_AVG                           -0.010114           0.266349   \n",
       "\n",
       "                            FLOORSMAX_MEDI  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "TARGET                           -0.043768                    0.000788   \n",
       "HOUR_APPR_PROCESS_START           0.119067                   -0.002985   \n",
       "NONLIVINGAREA_MODE                0.225478                   -0.005168   \n",
       "LIVINGAREA_MEDI                   0.626382                   -0.001125   \n",
       "DAYS_EMPLOYED                    -0.016051                    0.003072   \n",
       "...                                    ...                         ...   \n",
       "NONLIVINGAREA_AVG                 0.252893                   -0.006022   \n",
       "FLOORSMAX_MEDI                    1.000000                    0.000082   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK        0.000082                    1.000000   \n",
       "NONLIVINGAREA_MEDI                0.247247                   -0.006230   \n",
       "BASEMENTAREA_AVG                  0.327949                   -0.000650   \n",
       "\n",
       "                            NONLIVINGAREA_MEDI  BASEMENTAREA_AVG  \n",
       "TARGET                               -0.013337         -0.022746  \n",
       "HOUR_APPR_PROCESS_START               0.043729          0.041045  \n",
       "NONLIVINGAREA_MODE                    0.975839          0.260047  \n",
       "LIVINGAREA_MEDI                       0.298221          0.689318  \n",
       "DAYS_EMPLOYED                        -0.012975         -0.001382  \n",
       "...                                        ...               ...  \n",
       "NONLIVINGAREA_AVG                     0.990444          0.266349  \n",
       "FLOORSMAX_MEDI                        0.247247          0.327949  \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK           -0.006230         -0.000650  \n",
       "NONLIVINGAREA_MEDI                    1.000000          0.265382  \n",
       "BASEMENTAREA_AVG                      0.265382          1.000000  \n",
       "\n",
       "[71 rows x 71 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "corr_matrix = df_application_train_numerical.corr(method='pearson')\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 2s, sys: 3.82 s, total: 1min 6s\n",
      "Wall time: 1min 7s\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <th>...</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>ENTRANCES_AVG</th>\n",
       "      <th>ENTRANCES_MODE</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022914</td>\n",
       "      <td>-0.012511</td>\n",
       "      <td>-0.041140</td>\n",
       "      <td>0.025182</td>\n",
       "      <td>-0.027544</td>\n",
       "      <td>0.053711</td>\n",
       "      <td>-0.003666</td>\n",
       "      <td>-0.031177</td>\n",
       "      <td>-0.029212</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006896</td>\n",
       "      <td>-0.024194</td>\n",
       "      <td>-0.021635</td>\n",
       "      <td>-0.000073</td>\n",
       "      <td>0.032198</td>\n",
       "      <td>-0.013977</td>\n",
       "      <td>-0.046019</td>\n",
       "      <td>0.000736</td>\n",
       "      <td>-0.013150</td>\n",
       "      <td>-0.023249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <td>-0.022914</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030456</td>\n",
       "      <td>0.077421</td>\n",
       "      <td>-0.048260</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>-0.008099</td>\n",
       "      <td>0.014450</td>\n",
       "      <td>0.072522</td>\n",
       "      <td>0.064825</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022364</td>\n",
       "      <td>0.014529</td>\n",
       "      <td>0.008382</td>\n",
       "      <td>0.052475</td>\n",
       "      <td>-0.008282</td>\n",
       "      <td>0.039265</td>\n",
       "      <td>0.102058</td>\n",
       "      <td>-0.003696</td>\n",
       "      <td>0.036675</td>\n",
       "      <td>0.033080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <td>-0.012511</td>\n",
       "      <td>0.030456</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.192932</td>\n",
       "      <td>-0.004568</td>\n",
       "      <td>0.084740</td>\n",
       "      <td>-0.002368</td>\n",
       "      <td>0.699508</td>\n",
       "      <td>0.192218</td>\n",
       "      <td>0.211774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003871</td>\n",
       "      <td>0.135534</td>\n",
       "      <td>0.146924</td>\n",
       "      <td>0.033963</td>\n",
       "      <td>-0.009736</td>\n",
       "      <td>0.918531</td>\n",
       "      <td>0.197484</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.941964</td>\n",
       "      <td>0.167439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <td>-0.041140</td>\n",
       "      <td>0.077421</td>\n",
       "      <td>0.192932</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015972</td>\n",
       "      <td>0.490411</td>\n",
       "      <td>-0.010310</td>\n",
       "      <td>0.237970</td>\n",
       "      <td>0.890798</td>\n",
       "      <td>0.868653</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029653</td>\n",
       "      <td>0.608620</td>\n",
       "      <td>0.564812</td>\n",
       "      <td>0.081982</td>\n",
       "      <td>-0.019266</td>\n",
       "      <td>0.215271</td>\n",
       "      <td>0.779970</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>0.212738</td>\n",
       "      <td>0.716333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <td>0.025182</td>\n",
       "      <td>-0.048260</td>\n",
       "      <td>-0.004568</td>\n",
       "      <td>-0.015972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003510</td>\n",
       "      <td>0.130374</td>\n",
       "      <td>-0.006263</td>\n",
       "      <td>-0.013047</td>\n",
       "      <td>-0.012519</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.041931</td>\n",
       "      <td>-0.005295</td>\n",
       "      <td>-0.003780</td>\n",
       "      <td>-0.127772</td>\n",
       "      <td>0.018416</td>\n",
       "      <td>-0.006555</td>\n",
       "      <td>-0.013736</td>\n",
       "      <td>0.001444</td>\n",
       "      <td>-0.006729</td>\n",
       "      <td>-0.009152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <td>-0.013977</td>\n",
       "      <td>0.039265</td>\n",
       "      <td>0.918531</td>\n",
       "      <td>0.215271</td>\n",
       "      <td>-0.006555</td>\n",
       "      <td>0.102098</td>\n",
       "      <td>-0.000956</td>\n",
       "      <td>0.636881</td>\n",
       "      <td>0.221438</td>\n",
       "      <td>0.191678</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008381</td>\n",
       "      <td>0.133549</td>\n",
       "      <td>0.098139</td>\n",
       "      <td>0.048435</td>\n",
       "      <td>-0.013706</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.242681</td>\n",
       "      <td>-0.000911</td>\n",
       "      <td>0.980959</td>\n",
       "      <td>0.181035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <td>-0.046019</td>\n",
       "      <td>0.102058</td>\n",
       "      <td>0.197484</td>\n",
       "      <td>0.779970</td>\n",
       "      <td>-0.013736</td>\n",
       "      <td>0.578481</td>\n",
       "      <td>-0.006941</td>\n",
       "      <td>0.217045</td>\n",
       "      <td>0.723179</td>\n",
       "      <td>0.692775</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036999</td>\n",
       "      <td>0.188915</td>\n",
       "      <td>0.146297</td>\n",
       "      <td>0.103240</td>\n",
       "      <td>-0.028391</td>\n",
       "      <td>0.242681</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000849</td>\n",
       "      <td>0.233201</td>\n",
       "      <td>0.437873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <td>0.000736</td>\n",
       "      <td>-0.003696</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>0.001444</td>\n",
       "      <td>-0.002969</td>\n",
       "      <td>-0.008071</td>\n",
       "      <td>0.003196</td>\n",
       "      <td>-0.000650</td>\n",
       "      <td>-0.000636</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.027046</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.013199</td>\n",
       "      <td>-0.004267</td>\n",
       "      <td>-0.000911</td>\n",
       "      <td>0.000849</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000756</td>\n",
       "      <td>0.000618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <td>-0.013150</td>\n",
       "      <td>0.036675</td>\n",
       "      <td>0.941964</td>\n",
       "      <td>0.212738</td>\n",
       "      <td>-0.006729</td>\n",
       "      <td>0.100373</td>\n",
       "      <td>-0.002025</td>\n",
       "      <td>0.658727</td>\n",
       "      <td>0.214009</td>\n",
       "      <td>0.198000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007533</td>\n",
       "      <td>0.133327</td>\n",
       "      <td>0.107831</td>\n",
       "      <td>0.043722</td>\n",
       "      <td>-0.011583</td>\n",
       "      <td>0.980959</td>\n",
       "      <td>0.233201</td>\n",
       "      <td>-0.000756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.177307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <td>-0.023249</td>\n",
       "      <td>0.033080</td>\n",
       "      <td>0.167439</td>\n",
       "      <td>0.716333</td>\n",
       "      <td>-0.009152</td>\n",
       "      <td>0.315576</td>\n",
       "      <td>-0.004876</td>\n",
       "      <td>0.181079</td>\n",
       "      <td>0.692318</td>\n",
       "      <td>0.677336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016563</td>\n",
       "      <td>0.659649</td>\n",
       "      <td>0.614676</td>\n",
       "      <td>0.041750</td>\n",
       "      <td>-0.015361</td>\n",
       "      <td>0.181035</td>\n",
       "      <td>0.437873</td>\n",
       "      <td>0.000618</td>\n",
       "      <td>0.177307</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              TARGET  HOUR_APPR_PROCESS_START  \\\n",
       "TARGET                      1.000000                -0.022914   \n",
       "HOUR_APPR_PROCESS_START    -0.022914                 1.000000   \n",
       "NONLIVINGAREA_MODE         -0.012511                 0.030456   \n",
       "LIVINGAREA_MEDI            -0.041140                 0.077421   \n",
       "DAYS_EMPLOYED               0.025182                -0.048260   \n",
       "...                              ...                      ...   \n",
       "NONLIVINGAREA_AVG          -0.013977                 0.039265   \n",
       "FLOORSMAX_MEDI             -0.046019                 0.102058   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK  0.000736                -0.003696   \n",
       "NONLIVINGAREA_MEDI         -0.013150                 0.036675   \n",
       "BASEMENTAREA_AVG           -0.023249                 0.033080   \n",
       "\n",
       "                            NONLIVINGAREA_MODE  LIVINGAREA_MEDI  \\\n",
       "TARGET                               -0.012511        -0.041140   \n",
       "HOUR_APPR_PROCESS_START               0.030456         0.077421   \n",
       "NONLIVINGAREA_MODE                    1.000000         0.192932   \n",
       "LIVINGAREA_MEDI                       0.192932         1.000000   \n",
       "DAYS_EMPLOYED                        -0.004568        -0.015972   \n",
       "...                                        ...              ...   \n",
       "NONLIVINGAREA_AVG                     0.918531         0.215271   \n",
       "FLOORSMAX_MEDI                        0.197484         0.779970   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK            0.000264         0.000715   \n",
       "NONLIVINGAREA_MEDI                    0.941964         0.212738   \n",
       "BASEMENTAREA_AVG                      0.167439         0.716333   \n",
       "\n",
       "                            DAYS_EMPLOYED  YEARS_BEGINEXPLUATATION_MEDI  \\\n",
       "TARGET                           0.025182                     -0.027544   \n",
       "HOUR_APPR_PROCESS_START         -0.048260                      0.000252   \n",
       "NONLIVINGAREA_MODE              -0.004568                      0.084740   \n",
       "LIVINGAREA_MEDI                 -0.015972                      0.490411   \n",
       "DAYS_EMPLOYED                    1.000000                     -0.003510   \n",
       "...                                   ...                           ...   \n",
       "NONLIVINGAREA_AVG               -0.006555                      0.102098   \n",
       "FLOORSMAX_MEDI                  -0.013736                      0.578481   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK       0.001444                     -0.002969   \n",
       "NONLIVINGAREA_MEDI              -0.006729                      0.100373   \n",
       "BASEMENTAREA_AVG                -0.009152                      0.315576   \n",
       "\n",
       "                            DAYS_LAST_PHONE_CHANGE  NONLIVINGAPARTMENTS_MODE  \\\n",
       "TARGET                                    0.053711                 -0.003666   \n",
       "HOUR_APPR_PROCESS_START                  -0.008099                  0.014450   \n",
       "NONLIVINGAREA_MODE                       -0.002368                  0.699508   \n",
       "LIVINGAREA_MEDI                          -0.010310                  0.237970   \n",
       "DAYS_EMPLOYED                             0.130374                 -0.006263   \n",
       "...                                            ...                       ...   \n",
       "NONLIVINGAREA_AVG                        -0.000956                  0.636881   \n",
       "FLOORSMAX_MEDI                           -0.006941                  0.217045   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK               -0.008071                  0.003196   \n",
       "NONLIVINGAREA_MEDI                       -0.002025                  0.658727   \n",
       "BASEMENTAREA_AVG                         -0.004876                  0.181079   \n",
       "\n",
       "                            LIVINGAPARTMENTS_AVG  LIVINGAPARTMENTS_MODE  ...  \\\n",
       "TARGET                                 -0.031177              -0.029212  ...   \n",
       "HOUR_APPR_PROCESS_START                 0.072522               0.064825  ...   \n",
       "NONLIVINGAREA_MODE                      0.192218               0.211774  ...   \n",
       "LIVINGAREA_MEDI                         0.890798               0.868653  ...   \n",
       "DAYS_EMPLOYED                          -0.013047              -0.012519  ...   \n",
       "...                                          ...                    ...  ...   \n",
       "NONLIVINGAREA_AVG                       0.221438               0.191678  ...   \n",
       "FLOORSMAX_MEDI                          0.723179               0.692775  ...   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK             -0.000650              -0.000636  ...   \n",
       "NONLIVINGAREA_MEDI                      0.214009               0.198000  ...   \n",
       "BASEMENTAREA_AVG                        0.692318               0.677336  ...   \n",
       "\n",
       "                            AMT_REQ_CREDIT_BUREAU_MON  ENTRANCES_AVG  \\\n",
       "TARGET                                      -0.006896      -0.024194   \n",
       "HOUR_APPR_PROCESS_START                      0.022364       0.014529   \n",
       "NONLIVINGAREA_MODE                           0.003871       0.135534   \n",
       "LIVINGAREA_MEDI                              0.029653       0.608620   \n",
       "DAYS_EMPLOYED                               -0.041931      -0.005295   \n",
       "...                                               ...            ...   \n",
       "NONLIVINGAREA_AVG                            0.008381       0.133549   \n",
       "FLOORSMAX_MEDI                               0.036999       0.188915   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK                  -0.027046       0.000150   \n",
       "NONLIVINGAREA_MEDI                           0.007533       0.133327   \n",
       "BASEMENTAREA_AVG                             0.016563       0.659649   \n",
       "\n",
       "                            ENTRANCES_MODE  AMT_ANNUITY  \\\n",
       "TARGET                           -0.021635    -0.000073   \n",
       "HOUR_APPR_PROCESS_START           0.008382     0.052475   \n",
       "NONLIVINGAREA_MODE                0.146924     0.033963   \n",
       "LIVINGAREA_MEDI                   0.564812     0.081982   \n",
       "DAYS_EMPLOYED                    -0.003780    -0.127772   \n",
       "...                                    ...          ...   \n",
       "NONLIVINGAREA_AVG                 0.098139     0.048435   \n",
       "FLOORSMAX_MEDI                    0.146297     0.103240   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK        0.000233     0.013199   \n",
       "NONLIVINGAREA_MEDI                0.107831     0.043722   \n",
       "BASEMENTAREA_AVG                  0.614676     0.041750   \n",
       "\n",
       "                            DEF_30_CNT_SOCIAL_CIRCLE  NONLIVINGAREA_AVG  \\\n",
       "TARGET                                      0.032198          -0.013977   \n",
       "HOUR_APPR_PROCESS_START                    -0.008282           0.039265   \n",
       "NONLIVINGAREA_MODE                         -0.009736           0.918531   \n",
       "LIVINGAREA_MEDI                            -0.019266           0.215271   \n",
       "DAYS_EMPLOYED                               0.018416          -0.006555   \n",
       "...                                              ...                ...   \n",
       "NONLIVINGAREA_AVG                          -0.013706           1.000000   \n",
       "FLOORSMAX_MEDI                             -0.028391           0.242681   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK                 -0.004267          -0.000911   \n",
       "NONLIVINGAREA_MEDI                         -0.011583           0.980959   \n",
       "BASEMENTAREA_AVG                           -0.015361           0.181035   \n",
       "\n",
       "                            FLOORSMAX_MEDI  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "TARGET                           -0.046019                    0.000736   \n",
       "HOUR_APPR_PROCESS_START           0.102058                   -0.003696   \n",
       "NONLIVINGAREA_MODE                0.197484                    0.000264   \n",
       "LIVINGAREA_MEDI                   0.779970                    0.000715   \n",
       "DAYS_EMPLOYED                    -0.013736                    0.001444   \n",
       "...                                    ...                         ...   \n",
       "NONLIVINGAREA_AVG                 0.242681                   -0.000911   \n",
       "FLOORSMAX_MEDI                    1.000000                    0.000849   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK        0.000849                    1.000000   \n",
       "NONLIVINGAREA_MEDI                0.233201                   -0.000756   \n",
       "BASEMENTAREA_AVG                  0.437873                    0.000618   \n",
       "\n",
       "                            NONLIVINGAREA_MEDI  BASEMENTAREA_AVG  \n",
       "TARGET                               -0.013150         -0.023249  \n",
       "HOUR_APPR_PROCESS_START               0.036675          0.033080  \n",
       "NONLIVINGAREA_MODE                    0.941964          0.167439  \n",
       "LIVINGAREA_MEDI                       0.212738          0.716333  \n",
       "DAYS_EMPLOYED                        -0.006729         -0.009152  \n",
       "...                                        ...               ...  \n",
       "NONLIVINGAREA_AVG                     0.980959          0.181035  \n",
       "FLOORSMAX_MEDI                        0.233201          0.437873  \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK           -0.000756          0.000618  \n",
       "NONLIVINGAREA_MEDI                    1.000000          0.177307  \n",
       "BASEMENTAREA_AVG                      0.177307          1.000000  \n",
       "\n",
       "[71 rows x 71 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "spearman_corr_matrix = df_application_train_numerical.corr(method='spearman')\n",
    "spearman_corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dayyass/Desktop/bachelor_diploma/venv/lib/python3.9/site-packages/scipy/stats/stats.py:4594: RuntimeWarning: overflow encountered in long_scalars\n",
      "  (2 * xtie * ytie) / m + x0 * y0 / (9 * m * (size - 2)))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 9s, sys: 4.21 s, total: 1min 13s\n",
      "Wall time: 1min 16s\n"
     ]
    },
    {
     "data": {
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       "      <th>TARGET</th>\n",
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       "      <td>-0.025582</td>\n",
       "      <td>-0.023997</td>\n",
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       "      <td>-0.006782</td>\n",
       "      <td>-0.020873</td>\n",
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       "      <td>-0.019101</td>\n",
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       "      <td>0.006220</td>\n",
       "      <td>0.036444</td>\n",
       "      <td>-0.006977</td>\n",
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       "      <td>0.023066</td>\n",
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       "      <td>0.022751</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.139274</td>\n",
       "      <td>-0.003328</td>\n",
       "      <td>0.061502</td>\n",
       "      <td>-0.001709</td>\n",
       "      <td>0.618769</td>\n",
       "      <td>0.140294</td>\n",
       "      <td>0.154735</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003336</td>\n",
       "      <td>0.102761</td>\n",
       "      <td>0.112696</td>\n",
       "      <td>0.024419</td>\n",
       "      <td>-0.008486</td>\n",
       "      <td>0.886664</td>\n",
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       "      <td>0.023738</td>\n",
       "      <td>0.468269</td>\n",
       "      <td>0.437809</td>\n",
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       "      <td>-0.015594</td>\n",
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       "      <td>-0.006629</td>\n",
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       "      <td>-0.000533</td>\n",
       "      <td>-0.000522</td>\n",
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       "      <td>-0.026584</td>\n",
       "      <td>0.000129</td>\n",
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       "      <td>0.152185</td>\n",
       "      <td>-0.004847</td>\n",
       "      <td>0.072255</td>\n",
       "      <td>-0.001450</td>\n",
       "      <td>0.574502</td>\n",
       "      <td>0.154373</td>\n",
       "      <td>0.142961</td>\n",
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       "      <td>-0.009992</td>\n",
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       "      <td>-0.003294</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.013341</td>\n",
       "      <td>0.516745</td>\n",
       "      <td>0.482859</td>\n",
       "      <td>0.027993</td>\n",
       "      <td>-0.012510</td>\n",
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       "      <td>0.000507</td>\n",
       "      <td>0.128037</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 71 columns</p>\n",
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      ],
      "text/plain": [
       "                              TARGET  HOUR_APPR_PROCESS_START  \\\n",
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       "DAYS_EMPLOYED               0.020843                -0.033996   \n",
       "...                              ...                      ...   \n",
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       "\n",
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       "TARGET                               -0.011003        -0.033600   \n",
       "HOUR_APPR_PROCESS_START               0.022751         0.053724   \n",
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       "LIVINGAREA_MEDI                       0.139274         1.000000   \n",
       "DAYS_EMPLOYED                        -0.003328        -0.010754   \n",
       "...                                        ...              ...   \n",
       "NONLIVINGAREA_AVG                     0.886664         0.153553   \n",
       "FLOORSMAX_MEDI                        0.156196         0.642042   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK            0.000231         0.000584   \n",
       "NONLIVINGAREA_MEDI                    0.915297         0.152185   \n",
       "BASEMENTAREA_AVG                      0.122075         0.551078   \n",
       "\n",
       "                            DAYS_EMPLOYED  YEARS_BEGINEXPLUATATION_MEDI  \\\n",
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       "LIVINGAREA_MEDI                 -0.010754                      0.349934   \n",
       "DAYS_EMPLOYED                    1.000000                     -0.002380   \n",
       "...                                   ...                           ...   \n",
       "NONLIVINGAREA_AVG               -0.004704                      0.073201   \n",
       "FLOORSMAX_MEDI                  -0.010121                      0.460378   \n",
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       "\n",
       "                            DAYS_LAST_PHONE_CHANGE  NONLIVINGAPARTMENTS_MODE  \\\n",
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       "HOUR_APPR_PROCESS_START                  -0.005694                  0.011452   \n",
       "NONLIVINGAREA_MODE                       -0.001709                  0.618769   \n",
       "LIVINGAREA_MEDI                          -0.006925                  0.183288   \n",
       "DAYS_EMPLOYED                             0.091567                 -0.004827   \n",
       "...                                            ...                       ...   \n",
       "NONLIVINGAREA_AVG                        -0.000685                  0.552165   \n",
       "FLOORSMAX_MEDI                           -0.005105                  0.183036   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK               -0.006629                  0.002975   \n",
       "NONLIVINGAREA_MEDI                       -0.001450                  0.574502   \n",
       "BASEMENTAREA_AVG                         -0.003294                  0.139939   \n",
       "\n",
       "                            LIVINGAPARTMENTS_AVG  LIVINGAPARTMENTS_MODE  ...  \\\n",
       "TARGET                                 -0.025582              -0.023997  ...   \n",
       "HOUR_APPR_PROCESS_START                 0.050581               0.045257  ...   \n",
       "NONLIVINGAREA_MODE                      0.140294               0.154735  ...   \n",
       "LIVINGAREA_MEDI                         0.762621               0.736547  ...   \n",
       "DAYS_EMPLOYED                          -0.008822              -0.008481  ...   \n",
       "...                                          ...                    ...  ...   \n",
       "NONLIVINGAREA_AVG                       0.159035               0.137767  ...   \n",
       "FLOORSMAX_MEDI                          0.588105               0.559729  ...   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK             -0.000533              -0.000522  ...   \n",
       "NONLIVINGAREA_MEDI                      0.154373               0.142961  ...   \n",
       "BASEMENTAREA_AVG                        0.532971               0.519895  ...   \n",
       "\n",
       "                            AMT_REQ_CREDIT_BUREAU_MON  ENTRANCES_AVG  \\\n",
       "TARGET                                      -0.006782      -0.020873   \n",
       "HOUR_APPR_PROCESS_START                      0.018707       0.010650   \n",
       "NONLIVINGAREA_MODE                           0.003336       0.102761   \n",
       "LIVINGAREA_MEDI                              0.023738       0.468269   \n",
       "DAYS_EMPLOYED                               -0.034151      -0.003758   \n",
       "...                                               ...            ...   \n",
       "NONLIVINGAREA_AVG                            0.007121       0.100326   \n",
       "FLOORSMAX_MEDI                               0.032431       0.159242   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK                  -0.026584       0.000129   \n",
       "NONLIVINGAREA_MEDI                           0.006424       0.100372   \n",
       "BASEMENTAREA_AVG                             0.013341       0.516745   \n",
       "\n",
       "                            ENTRANCES_MODE  AMT_ANNUITY  \\\n",
       "TARGET                           -0.018855    -0.000060   \n",
       "HOUR_APPR_PROCESS_START           0.006220     0.036444   \n",
       "NONLIVINGAREA_MODE                0.112696     0.024419   \n",
       "LIVINGAREA_MEDI                   0.437809     0.054707   \n",
       "DAYS_EMPLOYED                    -0.002701    -0.086429   \n",
       "...                                    ...          ...   \n",
       "NONLIVINGAREA_AVG                 0.074160     0.034339   \n",
       "FLOORSMAX_MEDI                    0.128962     0.075488   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK        0.000203     0.010778   \n",
       "NONLIVINGAREA_MEDI                0.081803     0.031107   \n",
       "BASEMENTAREA_AVG                  0.482859     0.027993   \n",
       "\n",
       "                            DEF_30_CNT_SOCIAL_CIRCLE  NONLIVINGAREA_AVG  \\\n",
       "TARGET                                      0.031900          -0.012121   \n",
       "HOUR_APPR_PROCESS_START                    -0.006977           0.028913   \n",
       "NONLIVINGAREA_MODE                         -0.008486           0.886664   \n",
       "LIVINGAREA_MEDI                            -0.015594           0.153553   \n",
       "DAYS_EMPLOYED                               0.015103          -0.004704   \n",
       "...                                              ...                ...   \n",
       "NONLIVINGAREA_AVG                          -0.011781           1.000000   \n",
       "FLOORSMAX_MEDI                             -0.025153           0.189455   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK                 -0.004226          -0.000790   \n",
       "NONLIVINGAREA_MEDI                         -0.009992           0.966864   \n",
       "BASEMENTAREA_AVG                           -0.012510           0.130285   \n",
       "\n",
       "                            FLOORSMAX_MEDI  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "TARGET                           -0.041140                    0.000735   \n",
       "HOUR_APPR_PROCESS_START           0.077581                   -0.003143   \n",
       "NONLIVINGAREA_MODE                0.156196                    0.000231   \n",
       "LIVINGAREA_MEDI                   0.642042                    0.000584   \n",
       "DAYS_EMPLOYED                    -0.010121                    0.001194   \n",
       "...                                    ...                         ...   \n",
       "NONLIVINGAREA_AVG                 0.189455                   -0.000790   \n",
       "FLOORSMAX_MEDI                    1.000000                    0.000759   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK        0.000759                    1.000000   \n",
       "NONLIVINGAREA_MEDI                0.182612                   -0.000658   \n",
       "BASEMENTAREA_AVG                  0.339245                    0.000507   \n",
       "\n",
       "                            NONLIVINGAREA_MEDI  BASEMENTAREA_AVG  \n",
       "TARGET                               -0.011446         -0.019101  \n",
       "HOUR_APPR_PROCESS_START               0.027109          0.023066  \n",
       "NONLIVINGAREA_MODE                    0.915297          0.122075  \n",
       "LIVINGAREA_MEDI                       0.152185          0.551078  \n",
       "DAYS_EMPLOYED                        -0.004847         -0.006202  \n",
       "...                                        ...               ...  \n",
       "NONLIVINGAREA_AVG                     0.966864          0.130285  \n",
       "FLOORSMAX_MEDI                        0.182612          0.339245  \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK           -0.000658          0.000507  \n",
       "NONLIVINGAREA_MEDI                    1.000000          0.128037  \n",
       "BASEMENTAREA_AVG                      0.128037          1.000000  \n",
       "\n",
       "[71 rows x 71 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "kendall_corr_matrix = df_application_train_numerical.corr(method='kendall')\n",
    "kendall_corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 30))\n",
    "sns.heatmap(corr_matrix)\n",
    "plt.tick_params(axis='both', which='major', labelsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### test correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_columns = df_application_train_numerical.shape[1]\n",
    "n_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix_p_value = np.zeros((n_columns, n_columns))\n",
    "spearman_corr_matrix_p_value = np.zeros((n_columns, n_columns))\n",
    "kendall_corr_matrix_p_value = np.zeros((n_columns, n_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/71 [00:00<?, ?it/s]/Users/dayyass/Desktop/bachelor_diploma/analysis/stats_tests.py:69: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  t_statistic = corr_coef * np.sqrt(ddof) / np.sqrt(1 - corr_coef ** 2)\n",
      "100%|██████████| 71/71 [02:31<00:00,  2.14s/it]\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(n_columns)):\n",
    "    for j in range(n_columns):\n",
    "        \n",
    "        col_1 = df_application_train_numerical.iloc[:, i]\n",
    "        col_2 = df_application_train_numerical.iloc[:, j]\n",
    "        \n",
    "        n_samples = sum(~(col_1.isna() | col_2.isna()))\n",
    "        \n",
    "        corr = corr_matrix.iloc[i, j]\n",
    "        spearman_corr = spearman_corr_matrix.iloc[i, j]\n",
    "        kendall_corr = kendall_corr_matrix.iloc[i, j]\n",
    "        \n",
    "        corr_matrix_p_value[i, j] = pearson_spearman_significance_test(\n",
    "            corr_coef=corr,\n",
    "            n_samples=n_samples,\n",
    "            alternative='two-sided',\n",
    "        )[1]\n",
    "        spearman_corr_matrix_p_value[i, j] = pearson_spearman_significance_test(\n",
    "            corr_coef=spearman_corr,\n",
    "            n_samples=n_samples,\n",
    "            alternative='two-sided',\n",
    "        )[1]\n",
    "        kendall_corr_matrix_p_value[i, j] = kendall_significance_test(\n",
    "            kendall_coef=kendall_corr,\n",
    "            n_samples=n_samples,\n",
    "            alternative='two-sided',\n",
    "        )[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>TARGET</th>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <th>...</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>ENTRANCES_AVG</th>\n",
       "      <th>ENTRANCES_MODE</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.633206</td>\n",
       "      <td>5.773160e-15</td>\n",
       "      <td>2.911005e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>1.296012e-10</td>\n",
       "      <td>6.772360e-14</td>\n",
       "      <td>1.086620e-11</td>\n",
       "      <td>1.182610e-12</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.628886e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.684555</td>\n",
       "      <td>7.365257e-07</td>\n",
       "      <td>4.440892e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>1.702483e-10</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.687616e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.123670</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <td>2.370314e-06</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.340919e-05</td>\n",
       "      <td>0.013865</td>\n",
       "      <td>6.049282e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>3.598070e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.419585e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.072772</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.684449e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.391801e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.359242e-10</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.680604</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>7.684449e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000851</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.136164</td>\n",
       "      <td>2.986544e-10</td>\n",
       "      <td>1.007264e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.164392e-01</td>\n",
       "      <td>5.343137e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.808953e-06</td>\n",
       "      <td>2.805114e-10</td>\n",
       "      <td>0.113100</td>\n",
       "      <td>1.454997e-06</td>\n",
       "      <td>6.215777e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <td>4.628886e-07</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.808953e-06</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>5.263606e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.448463e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.543977e-05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.036550</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.805114e-10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.403979e-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.975876</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <td>6.845546e-01</td>\n",
       "      <td>0.12367</td>\n",
       "      <td>0.072772</td>\n",
       "      <td>6.806039e-01</td>\n",
       "      <td>1.130999e-01</td>\n",
       "      <td>0.292474</td>\n",
       "      <td>7.143055e-02</td>\n",
       "      <td>0.455027</td>\n",
       "      <td>2.799777e-01</td>\n",
       "      <td>3.728599e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>3.586020e-13</td>\n",
       "      <td>4.826568e-01</td>\n",
       "      <td>4.523817e-01</td>\n",
       "      <td>8.129053e-13</td>\n",
       "      <td>2.644599e-01</td>\n",
       "      <td>3.655019e-02</td>\n",
       "      <td>9.758760e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.052464e-02</td>\n",
       "      <td>8.280869e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <td>7.365257e-07</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.454997e-06</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>4.774018e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.636651e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.166463e-05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.030525</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <td>4.440892e-16</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.215777e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.058365e-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>4.567458e-13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.121114e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.828087</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  TARGET  HOUR_APPR_PROCESS_START  \\\n",
       "TARGET                      0.000000e+00                  0.00000   \n",
       "HOUR_APPR_PROCESS_START     0.000000e+00                  0.00000   \n",
       "NONLIVINGAREA_MODE          2.370314e-06                  0.00000   \n",
       "LIVINGAREA_MEDI             0.000000e+00                  0.00000   \n",
       "DAYS_EMPLOYED               0.000000e+00                  0.00000   \n",
       "...                                  ...                      ...   \n",
       "NONLIVINGAREA_AVG           4.628886e-07                  0.00000   \n",
       "FLOORSMAX_MEDI              0.000000e+00                  0.00000   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK  6.845546e-01                  0.12367   \n",
       "NONLIVINGAREA_MEDI          7.365257e-07                  0.00000   \n",
       "BASEMENTAREA_AVG            4.440892e-16                  0.00000   \n",
       "\n",
       "                            NONLIVINGAREA_MODE  LIVINGAREA_MEDI  \\\n",
       "TARGET                                0.000002     0.000000e+00   \n",
       "HOUR_APPR_PROCESS_START               0.000000     0.000000e+00   \n",
       "NONLIVINGAREA_MODE                    0.000000     0.000000e+00   \n",
       "LIVINGAREA_MEDI                       0.000000     0.000000e+00   \n",
       "DAYS_EMPLOYED                         0.000013     7.684449e-07   \n",
       "...                                        ...              ...   \n",
       "NONLIVINGAREA_AVG                     0.000000     0.000000e+00   \n",
       "FLOORSMAX_MEDI                        0.000000     0.000000e+00   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK            0.072772     6.806039e-01   \n",
       "NONLIVINGAREA_MEDI                    0.000000     0.000000e+00   \n",
       "BASEMENTAREA_AVG                      0.000000     0.000000e+00   \n",
       "\n",
       "                            DAYS_EMPLOYED  YEARS_BEGINEXPLUATATION_MEDI  \\\n",
       "TARGET                       0.000000e+00                      0.000073   \n",
       "HOUR_APPR_PROCESS_START      0.000000e+00                      0.000104   \n",
       "NONLIVINGAREA_MODE           1.340919e-05                      0.013865   \n",
       "LIVINGAREA_MEDI              7.684449e-07                      0.000000   \n",
       "DAYS_EMPLOYED                0.000000e+00                      0.000851   \n",
       "...                                   ...                           ...   \n",
       "NONLIVINGAREA_AVG            1.808953e-06                      0.000019   \n",
       "FLOORSMAX_MEDI               2.805114e-10                      0.000000   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK   1.130999e-01                      0.292474   \n",
       "NONLIVINGAREA_MEDI           1.454997e-06                      0.000092   \n",
       "BASEMENTAREA_AVG             6.215777e-01                      0.000000   \n",
       "\n",
       "                            DAYS_LAST_PHONE_CHANGE  NONLIVINGAPARTMENTS_MODE  \\\n",
       "TARGET                                0.000000e+00                  0.633206   \n",
       "HOUR_APPR_PROCESS_START               2.220446e-16                  0.000036   \n",
       "NONLIVINGAREA_MODE                    6.049282e-01                  0.000000   \n",
       "LIVINGAREA_MEDI                       2.391801e-05                  0.000000   \n",
       "DAYS_EMPLOYED                         0.000000e+00                  0.136164   \n",
       "...                                            ...                       ...   \n",
       "NONLIVINGAREA_AVG                     5.263606e-01                  0.000000   \n",
       "FLOORSMAX_MEDI                        5.403979e-03                  0.000000   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK            7.143055e-02                  0.455027   \n",
       "NONLIVINGAREA_MEDI                    4.774018e-01                  0.000000   \n",
       "BASEMENTAREA_AVG                      8.058365e-03                  0.000000   \n",
       "\n",
       "                            LIVINGAPARTMENTS_AVG  LIVINGAPARTMENTS_MODE  ...  \\\n",
       "TARGET                              5.773160e-15           2.911005e-13  ...   \n",
       "HOUR_APPR_PROCESS_START             0.000000e+00           0.000000e+00  ...   \n",
       "NONLIVINGAREA_MODE                  0.000000e+00           0.000000e+00  ...   \n",
       "LIVINGAREA_MEDI                     0.000000e+00           0.000000e+00  ...   \n",
       "DAYS_EMPLOYED                       2.986544e-10           1.007264e-09  ...   \n",
       "...                                          ...                    ...  ...   \n",
       "NONLIVINGAREA_AVG                   0.000000e+00           0.000000e+00  ...   \n",
       "FLOORSMAX_MEDI                      0.000000e+00           0.000000e+00  ...   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK          2.799777e-01           3.728599e-01  ...   \n",
       "NONLIVINGAREA_MEDI                  0.000000e+00           0.000000e+00  ...   \n",
       "BASEMENTAREA_AVG                    0.000000e+00           0.000000e+00  ...   \n",
       "\n",
       "                            AMT_REQ_CREDIT_BUREAU_MON  ENTRANCES_AVG  \\\n",
       "TARGET                                   1.296012e-10   6.772360e-14   \n",
       "HOUR_APPR_PROCESS_START                  0.000000e+00   2.220446e-16   \n",
       "NONLIVINGAREA_MODE                       3.598070e-04   0.000000e+00   \n",
       "LIVINGAREA_MEDI                          0.000000e+00   0.000000e+00   \n",
       "DAYS_EMPLOYED                            0.000000e+00   1.164392e-01   \n",
       "...                                               ...            ...   \n",
       "NONLIVINGAREA_AVG                        1.448463e-06   0.000000e+00   \n",
       "FLOORSMAX_MEDI                           0.000000e+00   0.000000e+00   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK               3.586020e-13   4.826568e-01   \n",
       "NONLIVINGAREA_MEDI                       7.636651e-06   0.000000e+00   \n",
       "BASEMENTAREA_AVG                         4.567458e-13   0.000000e+00   \n",
       "\n",
       "                            ENTRANCES_MODE   AMT_ANNUITY  \\\n",
       "TARGET                        1.086620e-11  1.182610e-12   \n",
       "HOUR_APPR_PROCESS_START       1.702483e-10  0.000000e+00   \n",
       "NONLIVINGAREA_MODE            0.000000e+00  0.000000e+00   \n",
       "LIVINGAREA_MEDI               0.000000e+00  0.000000e+00   \n",
       "DAYS_EMPLOYED                 5.343137e-02  0.000000e+00   \n",
       "...                                    ...           ...   \n",
       "NONLIVINGAREA_AVG             0.000000e+00  0.000000e+00   \n",
       "FLOORSMAX_MEDI                0.000000e+00  0.000000e+00   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK    4.523817e-01  8.129053e-13   \n",
       "NONLIVINGAREA_MEDI            0.000000e+00  0.000000e+00   \n",
       "BASEMENTAREA_AVG              0.000000e+00  0.000000e+00   \n",
       "\n",
       "                            DEF_30_CNT_SOCIAL_CIRCLE  NONLIVINGAREA_AVG  \\\n",
       "TARGET                                  0.000000e+00       4.628886e-07   \n",
       "HOUR_APPR_PROCESS_START                 1.687616e-03       0.000000e+00   \n",
       "NONLIVINGAREA_MODE                      2.419585e-04       0.000000e+00   \n",
       "LIVINGAREA_MEDI                         5.359242e-10       0.000000e+00   \n",
       "DAYS_EMPLOYED                           0.000000e+00       1.808953e-06   \n",
       "...                                              ...                ...   \n",
       "NONLIVINGAREA_AVG                       1.543977e-05       0.000000e+00   \n",
       "FLOORSMAX_MEDI                          0.000000e+00       0.000000e+00   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK              2.644599e-01       3.655019e-02   \n",
       "NONLIVINGAREA_MEDI                      4.166463e-05       0.000000e+00   \n",
       "BASEMENTAREA_AVG                        3.121114e-04       0.000000e+00   \n",
       "\n",
       "                            FLOORSMAX_MEDI  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "TARGET                        0.000000e+00                    0.684555   \n",
       "HOUR_APPR_PROCESS_START       0.000000e+00                    0.123670   \n",
       "NONLIVINGAREA_MODE            0.000000e+00                    0.072772   \n",
       "LIVINGAREA_MEDI               0.000000e+00                    0.680604   \n",
       "DAYS_EMPLOYED                 2.805114e-10                    0.113100   \n",
       "...                                    ...                         ...   \n",
       "NONLIVINGAREA_AVG             0.000000e+00                    0.036550   \n",
       "FLOORSMAX_MEDI                0.000000e+00                    0.975876   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK    9.758760e-01                    0.000000   \n",
       "NONLIVINGAREA_MEDI            0.000000e+00                    0.030525   \n",
       "BASEMENTAREA_AVG              0.000000e+00                    0.828087   \n",
       "\n",
       "                            NONLIVINGAREA_MEDI  BASEMENTAREA_AVG  \n",
       "TARGET                            7.365257e-07      4.440892e-16  \n",
       "HOUR_APPR_PROCESS_START           0.000000e+00      0.000000e+00  \n",
       "NONLIVINGAREA_MODE                0.000000e+00      0.000000e+00  \n",
       "LIVINGAREA_MEDI                   0.000000e+00      0.000000e+00  \n",
       "DAYS_EMPLOYED                     1.454997e-06      6.215777e-01  \n",
       "...                                        ...               ...  \n",
       "NONLIVINGAREA_AVG                 0.000000e+00      0.000000e+00  \n",
       "FLOORSMAX_MEDI                    0.000000e+00      0.000000e+00  \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK        3.052464e-02      8.280869e-01  \n",
       "NONLIVINGAREA_MEDI                0.000000e+00      0.000000e+00  \n",
       "BASEMENTAREA_AVG                  0.000000e+00      0.000000e+00  \n",
       "\n",
       "[71 rows x 71 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix_p_value = pd.DataFrame(\n",
    "    corr_matrix_p_value,\n",
    "    index=df_application_train_numerical.columns,\n",
    "    columns=df_application_train_numerical.columns,\n",
    ")\n",
    "corr_matrix_p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <th>...</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>ENTRANCES_AVG</th>\n",
       "      <th>ENTRANCES_MODE</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.261004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.757884e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.675619e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.112423e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.704276</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>2.220446e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.920305</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.368371e-08</td>\n",
       "      <td>0.001055</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.543559e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.056618</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <td>3.399716e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.994020e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.379355</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.789728e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.096681e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.927105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.073986e-10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.262457e-14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.793548</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.089940</td>\n",
       "      <td>4.073986e-10</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.163594</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.054828</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.856193e-02</td>\n",
       "      <td>0.139623</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.495211e-02</td>\n",
       "      <td>6.699715e-08</td>\n",
       "      <td>0.456428</td>\n",
       "      <td>0.012487</td>\n",
       "      <td>1.079790e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <td>2.112423e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.495211e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.722625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.614170e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.814945e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.751864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.699715e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006365</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.754896</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <td>7.042763e-01</td>\n",
       "      <td>0.056618</td>\n",
       "      <td>0.927105</td>\n",
       "      <td>7.935481e-01</td>\n",
       "      <td>4.564281e-01</td>\n",
       "      <td>0.270680</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.359254</td>\n",
       "      <td>0.849510</td>\n",
       "      <td>0.852855</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.562900e-01</td>\n",
       "      <td>0.932009</td>\n",
       "      <td>9.934942e-12</td>\n",
       "      <td>2.799851e-02</td>\n",
       "      <td>7.518639e-01</td>\n",
       "      <td>7.548963e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.793051</td>\n",
       "      <td>8.365446e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <td>1.048965e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.248737e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.452248</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.907387e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.778109e-05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.793051</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.079790e-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081581</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.130392e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.362387e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.836545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  TARGET  HOUR_APPR_PROCESS_START  \\\n",
       "TARGET                      0.000000e+00                 0.000000   \n",
       "HOUR_APPR_PROCESS_START     0.000000e+00                 0.000000   \n",
       "NONLIVINGAREA_MODE          3.399716e-06                 0.000000   \n",
       "LIVINGAREA_MEDI             0.000000e+00                 0.000000   \n",
       "DAYS_EMPLOYED               0.000000e+00                 0.000000   \n",
       "...                                  ...                      ...   \n",
       "NONLIVINGAREA_AVG           2.112423e-07                 0.000000   \n",
       "FLOORSMAX_MEDI              0.000000e+00                 0.000000   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK  7.042763e-01                 0.056618   \n",
       "NONLIVINGAREA_MEDI          1.048965e-06                 0.000000   \n",
       "BASEMENTAREA_AVG            2.220446e-16                 0.000000   \n",
       "\n",
       "                            NONLIVINGAREA_MODE  LIVINGAREA_MEDI  \\\n",
       "TARGET                                0.000003     0.000000e+00   \n",
       "HOUR_APPR_PROCESS_START               0.000000     0.000000e+00   \n",
       "NONLIVINGAREA_MODE                    0.000000     0.000000e+00   \n",
       "LIVINGAREA_MEDI                       0.000000     0.000000e+00   \n",
       "DAYS_EMPLOYED                         0.089940     4.073986e-10   \n",
       "...                                        ...              ...   \n",
       "NONLIVINGAREA_AVG                     0.000000     0.000000e+00   \n",
       "FLOORSMAX_MEDI                        0.000000     0.000000e+00   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK            0.927105     7.935481e-01   \n",
       "NONLIVINGAREA_MEDI                    0.000000     0.000000e+00   \n",
       "BASEMENTAREA_AVG                      0.000000     0.000000e+00   \n",
       "\n",
       "                            DAYS_EMPLOYED  YEARS_BEGINEXPLUATATION_MEDI  \\\n",
       "TARGET                       0.000000e+00                      0.000000   \n",
       "HOUR_APPR_PROCESS_START      0.000000e+00                      0.920305   \n",
       "NONLIVINGAREA_MODE           8.994020e-02                      0.000000   \n",
       "LIVINGAREA_MEDI              4.073986e-10                      0.000000   \n",
       "DAYS_EMPLOYED                0.000000e+00                      0.163594   \n",
       "...                                   ...                           ...   \n",
       "NONLIVINGAREA_AVG            1.495211e-02                      0.000000   \n",
       "FLOORSMAX_MEDI               6.699715e-08                      0.000000   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK   4.564281e-01                      0.270680   \n",
       "NONLIVINGAREA_MEDI           1.248737e-02                      0.000000   \n",
       "BASEMENTAREA_AVG             1.079790e-03                      0.000000   \n",
       "\n",
       "                            DAYS_LAST_PHONE_CHANGE  NONLIVINGAPARTMENTS_MODE  \\\n",
       "TARGET                                    0.000000                  0.261004   \n",
       "HOUR_APPR_PROCESS_START                   0.000007                  0.000009   \n",
       "NONLIVINGAREA_MODE                        0.379355                  0.000000   \n",
       "LIVINGAREA_MEDI                           0.000055                  0.000000   \n",
       "DAYS_EMPLOYED                             0.000000                  0.054828   \n",
       "...                                            ...                       ...   \n",
       "NONLIVINGAREA_AVG                         0.722625                  0.000000   \n",
       "FLOORSMAX_MEDI                            0.006365                  0.000000   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK                0.000031                  0.359254   \n",
       "NONLIVINGAREA_MEDI                        0.452248                  0.000000   \n",
       "BASEMENTAREA_AVG                          0.081581                  0.000000   \n",
       "\n",
       "                            LIVINGAPARTMENTS_AVG  LIVINGAPARTMENTS_MODE  ...  \\\n",
       "TARGET                                  0.000000               0.000000  ...   \n",
       "HOUR_APPR_PROCESS_START                 0.000000               0.000000  ...   \n",
       "NONLIVINGAREA_MODE                      0.000000               0.000000  ...   \n",
       "LIVINGAREA_MEDI                         0.000000               0.000000  ...   \n",
       "DAYS_EMPLOYED                           0.000047               0.000094  ...   \n",
       "...                                          ...                    ...  ...   \n",
       "NONLIVINGAREA_AVG                       0.000000               0.000000  ...   \n",
       "FLOORSMAX_MEDI                          0.000000               0.000000  ...   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK              0.849510               0.852855  ...   \n",
       "NONLIVINGAREA_MEDI                      0.000000               0.000000  ...   \n",
       "BASEMENTAREA_AVG                        0.000000               0.000000  ...   \n",
       "\n",
       "                            AMT_REQ_CREDIT_BUREAU_MON  ENTRANCES_AVG  \\\n",
       "TARGET                                   3.757884e-04   0.000000e+00   \n",
       "HOUR_APPR_PROCESS_START                  0.000000e+00   1.368371e-08   \n",
       "NONLIVINGAREA_MODE                       1.789728e-01   0.000000e+00   \n",
       "LIVINGAREA_MEDI                          0.000000e+00   0.000000e+00   \n",
       "DAYS_EMPLOYED                            0.000000e+00   3.856193e-02   \n",
       "...                                               ...            ...   \n",
       "NONLIVINGAREA_AVG                        3.614170e-03   0.000000e+00   \n",
       "FLOORSMAX_MEDI                           0.000000e+00   0.000000e+00   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK               0.000000e+00   9.562900e-01   \n",
       "NONLIVINGAREA_MEDI                       8.907387e-03   0.000000e+00   \n",
       "BASEMENTAREA_AVG                         3.130392e-08   0.000000e+00   \n",
       "\n",
       "                            ENTRANCES_MODE   AMT_ANNUITY  \\\n",
       "TARGET                            0.000000  9.675619e-01   \n",
       "HOUR_APPR_PROCESS_START           0.001055  0.000000e+00   \n",
       "NONLIVINGAREA_MODE                0.000000  0.000000e+00   \n",
       "LIVINGAREA_MEDI                   0.000000  0.000000e+00   \n",
       "DAYS_EMPLOYED                     0.139623  0.000000e+00   \n",
       "...                                    ...           ...   \n",
       "NONLIVINGAREA_AVG                 0.000000  0.000000e+00   \n",
       "FLOORSMAX_MEDI                    0.000000  0.000000e+00   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK        0.932009  9.934942e-12   \n",
       "NONLIVINGAREA_MEDI                0.000000  0.000000e+00   \n",
       "BASEMENTAREA_AVG                  0.000000  0.000000e+00   \n",
       "\n",
       "                            DEF_30_CNT_SOCIAL_CIRCLE  NONLIVINGAREA_AVG  \\\n",
       "TARGET                                  0.000000e+00       2.112423e-07   \n",
       "HOUR_APPR_PROCESS_START                 4.543559e-06       0.000000e+00   \n",
       "NONLIVINGAREA_MODE                      3.096681e-04       0.000000e+00   \n",
       "LIVINGAREA_MEDI                         5.262457e-14       0.000000e+00   \n",
       "DAYS_EMPLOYED                           0.000000e+00       1.495211e-02   \n",
       "...                                              ...                ...   \n",
       "NONLIVINGAREA_AVG                       3.814945e-07       0.000000e+00   \n",
       "FLOORSMAX_MEDI                          0.000000e+00       0.000000e+00   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK              2.799851e-02       7.518639e-01   \n",
       "NONLIVINGAREA_MEDI                      1.778109e-05       0.000000e+00   \n",
       "BASEMENTAREA_AVG                        4.362387e-08       0.000000e+00   \n",
       "\n",
       "                            FLOORSMAX_MEDI  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "TARGET                        0.000000e+00                    0.704276   \n",
       "HOUR_APPR_PROCESS_START       0.000000e+00                    0.056618   \n",
       "NONLIVINGAREA_MODE            0.000000e+00                    0.927105   \n",
       "LIVINGAREA_MEDI               0.000000e+00                    0.793548   \n",
       "DAYS_EMPLOYED                 6.699715e-08                    0.456428   \n",
       "...                                    ...                         ...   \n",
       "NONLIVINGAREA_AVG             0.000000e+00                    0.751864   \n",
       "FLOORSMAX_MEDI                0.000000e+00                    0.754896   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK    7.548963e-01                    0.000000   \n",
       "NONLIVINGAREA_MEDI            0.000000e+00                    0.793051   \n",
       "BASEMENTAREA_AVG              0.000000e+00                    0.836545   \n",
       "\n",
       "                            NONLIVINGAREA_MEDI  BASEMENTAREA_AVG  \n",
       "TARGET                                0.000001      2.220446e-16  \n",
       "HOUR_APPR_PROCESS_START               0.000000      0.000000e+00  \n",
       "NONLIVINGAREA_MODE                    0.000000      0.000000e+00  \n",
       "LIVINGAREA_MEDI                       0.000000      0.000000e+00  \n",
       "DAYS_EMPLOYED                         0.012487      1.079790e-03  \n",
       "...                                        ...               ...  \n",
       "NONLIVINGAREA_AVG                     0.000000      0.000000e+00  \n",
       "FLOORSMAX_MEDI                        0.000000      0.000000e+00  \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK            0.793051      8.365446e-01  \n",
       "NONLIVINGAREA_MEDI                    0.000000      0.000000e+00  \n",
       "BASEMENTAREA_AVG                      0.000000      0.000000e+00  \n",
       "\n",
       "[71 rows x 71 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spearman_corr_matrix_p_value = pd.DataFrame(\n",
    "    spearman_corr_matrix_p_value,\n",
    "    index=df_application_train_numerical.columns,\n",
    "    columns=df_application_train_numerical.columns,\n",
    ")\n",
    "spearman_corr_matrix_p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <th>...</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>ENTRANCES_AVG</th>\n",
       "      <th>ENTRANCES_MODE</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.161543e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.549507e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.960249</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.479483e-11</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.569364</td>\n",
       "      <td>1.843357e-10</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.917093</td>\n",
       "      <td>2.174977e-06</td>\n",
       "      <td>1.389181e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.313709e-10</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.875697e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.015045</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <td>8.926042e-10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.386858e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.411996e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.231655e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.407505e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.904054</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.742049e-10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.801929e-05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.748666</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>6.386858e-02</td>\n",
       "      <td>2.742049e-10</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.156457</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.644005e-02</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.762752e-02</td>\n",
       "      <td>0.113438</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.803396e-03</td>\n",
       "      <td>2.418762e-09</td>\n",
       "      <td>0.355482</td>\n",
       "      <td>6.957049e-03</td>\n",
       "      <td>0.000891</td>\n",
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       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <td>1.479483e-11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.803396e-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.029572e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.084154e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.881895e-11</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.680856</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.418762e-09</td>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.675587</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <td>5.693637e-01</td>\n",
       "      <td>0.015045</td>\n",
       "      <td>9.040535e-01</td>\n",
       "      <td>7.486660e-01</td>\n",
       "      <td>3.554815e-01</td>\n",
       "      <td>0.173546</td>\n",
       "      <td>2.924942e-07</td>\n",
       "      <td>2.004537e-01</td>\n",
       "      <td>0.815461</td>\n",
       "      <td>0.819340</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.434941e-01</td>\n",
       "      <td>0.911328</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.099727e-03</td>\n",
       "      <td>6.808564e-01</td>\n",
       "      <td>6.755873e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.319780e-01</td>\n",
       "      <td>0.799455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <td>1.843357e-10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.957049e-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.194980e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.208639e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.819690e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.731978</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.914294e-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.756455e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.296674e-11</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.259282e-11</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.799455</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 71 columns</p>\n",
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       "                                  TARGET  HOUR_APPR_PROCESS_START  \\\n",
       "TARGET                      0.000000e+00                 0.000000   \n",
       "HOUR_APPR_PROCESS_START     0.000000e+00                 0.000000   \n",
       "NONLIVINGAREA_MODE          8.926042e-10                 0.000000   \n",
       "LIVINGAREA_MEDI             0.000000e+00                 0.000000   \n",
       "DAYS_EMPLOYED               0.000000e+00                 0.000000   \n",
       "...                                  ...                      ...   \n",
       "NONLIVINGAREA_AVG           1.479483e-11                 0.000000   \n",
       "FLOORSMAX_MEDI              0.000000e+00                 0.000000   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK  5.693637e-01                 0.015045   \n",
       "NONLIVINGAREA_MEDI          1.843357e-10                 0.000000   \n",
       "BASEMENTAREA_AVG            0.000000e+00                 0.000000   \n",
       "\n",
       "                            NONLIVINGAREA_MODE  LIVINGAREA_MEDI  \\\n",
       "TARGET                            8.926042e-10     0.000000e+00   \n",
       "HOUR_APPR_PROCESS_START           0.000000e+00     0.000000e+00   \n",
       "NONLIVINGAREA_MODE                0.000000e+00     0.000000e+00   \n",
       "LIVINGAREA_MEDI                   0.000000e+00     0.000000e+00   \n",
       "DAYS_EMPLOYED                     6.386858e-02     2.742049e-10   \n",
       "...                                        ...              ...   \n",
       "NONLIVINGAREA_AVG                 0.000000e+00     0.000000e+00   \n",
       "FLOORSMAX_MEDI                    0.000000e+00     0.000000e+00   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK        9.040535e-01     7.486660e-01   \n",
       "NONLIVINGAREA_MEDI                0.000000e+00     0.000000e+00   \n",
       "BASEMENTAREA_AVG                  0.000000e+00     0.000000e+00   \n",
       "\n",
       "                            DAYS_EMPLOYED  YEARS_BEGINEXPLUATATION_MEDI  \\\n",
       "TARGET                       0.000000e+00                      0.000000   \n",
       "HOUR_APPR_PROCESS_START      0.000000e+00                      0.917093   \n",
       "NONLIVINGAREA_MODE           6.386858e-02                      0.000000   \n",
       "LIVINGAREA_MEDI              2.742049e-10                      0.000000   \n",
       "DAYS_EMPLOYED                0.000000e+00                      0.156457   \n",
       "...                                   ...                           ...   \n",
       "NONLIVINGAREA_AVG            8.803396e-03                      0.000000   \n",
       "FLOORSMAX_MEDI               2.418762e-09                      0.000000   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK   3.554815e-01                      0.173546   \n",
       "NONLIVINGAREA_MEDI           6.957049e-03                      0.000000   \n",
       "BASEMENTAREA_AVG             8.914294e-04                      0.000000   \n",
       "\n",
       "                            DAYS_LAST_PHONE_CHANGE  NONLIVINGAPARTMENTS_MODE  \\\n",
       "TARGET                                0.000000e+00              1.161543e-01   \n",
       "HOUR_APPR_PROCESS_START               2.174977e-06              1.389181e-07   \n",
       "NONLIVINGAREA_MODE                    3.411996e-01              0.000000e+00   \n",
       "LIVINGAREA_MEDI                       4.801929e-05              0.000000e+00   \n",
       "DAYS_EMPLOYED                         0.000000e+00              2.644005e-02   \n",
       "...                                            ...                       ...   \n",
       "NONLIVINGAREA_AVG                     7.029572e-01              0.000000e+00   \n",
       "FLOORSMAX_MEDI                        2.612874e-03              0.000000e+00   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK            2.924942e-07              2.004537e-01   \n",
       "NONLIVINGAREA_MEDI                    4.194980e-01              0.000000e+00   \n",
       "BASEMENTAREA_AVG                      7.756455e-02              0.000000e+00   \n",
       "\n",
       "                            LIVINGAPARTMENTS_AVG  LIVINGAPARTMENTS_MODE  ...  \\\n",
       "TARGET                                  0.000000               0.000000  ...   \n",
       "HOUR_APPR_PROCESS_START                 0.000000               0.000000  ...   \n",
       "NONLIVINGAREA_MODE                      0.000000               0.000000  ...   \n",
       "LIVINGAREA_MEDI                         0.000000               0.000000  ...   \n",
       "DAYS_EMPLOYED                           0.000037               0.000072  ...   \n",
       "...                                          ...                    ...  ...   \n",
       "NONLIVINGAREA_AVG                       0.000000               0.000000  ...   \n",
       "FLOORSMAX_MEDI                          0.000000               0.000000  ...   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK              0.815461               0.819340  ...   \n",
       "NONLIVINGAREA_MEDI                      0.000000               0.000000  ...   \n",
       "BASEMENTAREA_AVG                        0.000000               0.000000  ...   \n",
       "\n",
       "                            AMT_REQ_CREDIT_BUREAU_MON  ENTRANCES_AVG  \\\n",
       "TARGET                                   1.549507e-07   0.000000e+00   \n",
       "HOUR_APPR_PROCESS_START                  0.000000e+00   4.313709e-10   \n",
       "NONLIVINGAREA_MODE                       8.231655e-02   0.000000e+00   \n",
       "LIVINGAREA_MEDI                          0.000000e+00   0.000000e+00   \n",
       "DAYS_EMPLOYED                            0.000000e+00   2.762752e-02   \n",
       "...                                               ...            ...   \n",
       "NONLIVINGAREA_AVG                        2.084154e-04   0.000000e+00   \n",
       "FLOORSMAX_MEDI                           0.000000e+00   0.000000e+00   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK               0.000000e+00   9.434941e-01   \n",
       "NONLIVINGAREA_MEDI                       8.208639e-04   0.000000e+00   \n",
       "BASEMENTAREA_AVG                         2.296674e-11   0.000000e+00   \n",
       "\n",
       "                            ENTRANCES_MODE  AMT_ANNUITY  \\\n",
       "TARGET                            0.000000     0.960249   \n",
       "HOUR_APPR_PROCESS_START           0.000267     0.000000   \n",
       "NONLIVINGAREA_MODE                0.000000     0.000000   \n",
       "LIVINGAREA_MEDI                   0.000000     0.000000   \n",
       "DAYS_EMPLOYED                     0.113438     0.000000   \n",
       "...                                    ...          ...   \n",
       "NONLIVINGAREA_AVG                 0.000000     0.000000   \n",
       "FLOORSMAX_MEDI                    0.000000     0.000000   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK        0.911328     0.000000   \n",
       "NONLIVINGAREA_MEDI                0.000000     0.000000   \n",
       "BASEMENTAREA_AVG                  0.000000     0.000000   \n",
       "\n",
       "                            DEF_30_CNT_SOCIAL_CIRCLE  NONLIVINGAREA_AVG  \\\n",
       "TARGET                                  0.000000e+00       1.479483e-11   \n",
       "HOUR_APPR_PROCESS_START                 6.875697e-09       0.000000e+00   \n",
       "NONLIVINGAREA_MODE                      2.407505e-06       0.000000e+00   \n",
       "LIVINGAREA_MEDI                         0.000000e+00       0.000000e+00   \n",
       "DAYS_EMPLOYED                           0.000000e+00       8.803396e-03   \n",
       "...                                              ...                ...   \n",
       "NONLIVINGAREA_AVG                       5.881895e-11       0.000000e+00   \n",
       "FLOORSMAX_MEDI                          0.000000e+00       0.000000e+00   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK              1.099727e-03       6.808564e-01   \n",
       "NONLIVINGAREA_MEDI                      2.819690e-08       0.000000e+00   \n",
       "BASEMENTAREA_AVG                        2.259282e-11       0.000000e+00   \n",
       "\n",
       "                            FLOORSMAX_MEDI  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "TARGET                        0.000000e+00                    0.569364   \n",
       "HOUR_APPR_PROCESS_START       0.000000e+00                    0.015045   \n",
       "NONLIVINGAREA_MODE            0.000000e+00                    0.904054   \n",
       "LIVINGAREA_MEDI               0.000000e+00                    0.748666   \n",
       "DAYS_EMPLOYED                 2.418762e-09                    0.355482   \n",
       "...                                    ...                         ...   \n",
       "NONLIVINGAREA_AVG             0.000000e+00                    0.680856   \n",
       "FLOORSMAX_MEDI                0.000000e+00                    0.675587   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK    6.755873e-01                    0.000000   \n",
       "NONLIVINGAREA_MEDI            0.000000e+00                    0.731978   \n",
       "BASEMENTAREA_AVG              0.000000e+00                    0.799455   \n",
       "\n",
       "                            NONLIVINGAREA_MEDI  BASEMENTAREA_AVG  \n",
       "TARGET                            1.843357e-10          0.000000  \n",
       "HOUR_APPR_PROCESS_START           0.000000e+00          0.000000  \n",
       "NONLIVINGAREA_MODE                0.000000e+00          0.000000  \n",
       "LIVINGAREA_MEDI                   0.000000e+00          0.000000  \n",
       "DAYS_EMPLOYED                     6.957049e-03          0.000891  \n",
       "...                                        ...               ...  \n",
       "NONLIVINGAREA_AVG                 0.000000e+00          0.000000  \n",
       "FLOORSMAX_MEDI                    0.000000e+00          0.000000  \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK        7.319780e-01          0.799455  \n",
       "NONLIVINGAREA_MEDI                0.000000e+00          0.000000  \n",
       "BASEMENTAREA_AVG                  0.000000e+00          0.000000  \n",
       "\n",
       "[71 rows x 71 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kendall_corr_matrix_p_value = pd.DataFrame(\n",
    "    kendall_corr_matrix_p_value,\n",
    "    index=df_application_train_numerical.columns,\n",
    "    columns=df_application_train_numerical.columns,\n",
    ")\n",
    "kendall_corr_matrix_p_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### target unnormalized correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HOUR_APPR_PROCESS_START target corr: -0.29\n",
      "NONLIVINGAREA_MODE target corr: -0.004\n",
      "LIVINGAREA_MEDI target corr: -0.014\n",
      "DAYS_EMPLOYED target corr: -23301.471\n",
      "YEARS_BEGINEXPLUATATION_MEDI target corr: -0.002\n",
      "DAYS_LAST_PHONE_CHANGE target corr: 167.591\n",
      "NONLIVINGAPARTMENTS_MODE target corr: -0.0\n",
      "LIVINGAPARTMENTS_AVG target corr: -0.009\n",
      "LIVINGAPARTMENTS_MODE target corr: -0.009\n",
      "FLOORSMIN_AVG target corr: -0.021\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR target corr: 0.14\n",
      "FLOORSMAX_MODE target corr: -0.024\n",
      "EXT_SOURCE_1 target corr: -0.124\n",
      "NONLIVINGAPARTMENTS_MEDI target corr: -0.001\n",
      "ENTRANCES_MEDI target corr: -0.008\n",
      "APARTMENTS_MODE target corr: -0.012\n",
      "COMMONAREA_MODE target corr: -0.005\n",
      "LANDAREA_MODE target corr: -0.003\n",
      "LIVINGAPARTMENTS_MEDI target corr: -0.009\n",
      "ELEVATORS_MEDI target corr: -0.018\n",
      "ELEVATORS_MODE target corr: -0.017\n",
      "REGION_POPULATION_RELATIVE target corr: -0.002\n",
      "OBS_30_CNT_SOCIAL_CIRCLE target corr: 0.08\n",
      "NONLIVINGAPARTMENTS_AVG target corr: -0.001\n",
      "COMMONAREA_AVG target corr: -0.006\n",
      "YEARS_BEGINEXPLUATATION_AVG target corr: -0.002\n",
      "AMT_REQ_CREDIT_BUREAU_QRT target corr: -0.006\n",
      "BASEMENTAREA_MEDI target corr: -0.007\n",
      "FLOORSMAX_AVG target corr: -0.025\n",
      "DAYS_BIRTH target corr: 1253.348\n",
      "BASEMENTAREA_MODE target corr: -0.007\n",
      "DAYS_REGISTRATION target corr: 542.814\n",
      "OWN_CAR_AGE target corr: 1.733\n",
      "FLOORSMIN_MEDI target corr: -0.021\n",
      "YEARS_BUILD_MEDI target corr: -0.01\n",
      "APARTMENTS_MEDI target corr: -0.013\n",
      "ELEVATORS_AVG target corr: -0.018\n",
      "AMT_REQ_CREDIT_BUREAU_HOUR target corr: 0.0\n",
      "LANDAREA_AVG target corr: -0.003\n",
      "AMT_GOODS_PRICE target corr: -53764.382\n",
      "APARTMENTS_AVG target corr: -0.013\n",
      "OBS_60_CNT_SOCIAL_CIRCLE target corr: 0.079\n",
      "YEARS_BUILD_AVG target corr: -0.01\n",
      "AMT_CREDIT target corr: -44869.754\n",
      "TOTALAREA_MODE target corr: -0.014\n",
      "EXT_SOURCE_3 target corr: -0.13\n",
      "YEARS_BUILD_MODE target corr: -0.01\n",
      "LIVINGAREA_MODE target corr: -0.013\n",
      "LANDAREA_MEDI target corr: -0.004\n",
      "AMT_INCOME_TOTAL target corr: -3465.961\n",
      "FLOORSMIN_MODE target corr: -0.021\n",
      "LIVINGAREA_AVG target corr: -0.014\n",
      "YEARS_BEGINEXPLUATATION_MODE target corr: -0.002\n",
      "AMT_REQ_CREDIT_BUREAU_DAY target corr: 0.001\n",
      "DEF_60_CNT_SOCIAL_CIRCLE target corr: 0.042\n",
      "EXT_SOURCE_2 target corr: -0.113\n",
      "CNT_CHILDREN target corr: 0.051\n",
      "DAYS_ID_PUBLISH target corr: 285.12\n",
      "COMMONAREA_MEDI target corr: -0.006\n",
      "CNT_FAM_MEMBERS target corr: 0.031\n",
      "AMT_REQ_CREDIT_BUREAU_MON target corr: -0.043\n",
      "ENTRANCES_AVG target corr: -0.008\n",
      "ENTRANCES_MODE target corr: -0.007\n",
      "AMT_ANNUITY target corr: -681.879\n",
      "DEF_30_CNT_SOCIAL_CIRCLE target corr: 0.053\n",
      "NONLIVINGAREA_AVG target corr: -0.004\n",
      "FLOORSMAX_MEDI target corr: -0.025\n",
      "AMT_REQ_CREDIT_BUREAU_WEEK target corr: 0.001\n",
      "NONLIVINGAREA_MEDI target corr: -0.004\n",
      "BASEMENTAREA_AVG target corr: -0.007\n"
     ]
    }
   ],
   "source": [
    "for column in df_application_train_numerical.columns[1:]:\n",
    "    target_filter = df_application_train_numerical['TARGET'] == 1\n",
    "    \n",
    "    feature_mean_target_1 = df_application_train_numerical[column][target_filter].mean()\n",
    "    feature_mean_target_0 = df_application_train_numerical[column][~target_filter].mean()\n",
    "    \n",
    "    target_unnormalized_correlation = feature_mean_target_1 - feature_mean_target_0\n",
    "    \n",
    "    print(f\"{column} target corr: {round(target_unnormalized_correlation, 3)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>TARGET</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>greater_median</th>\n",
       "      <td>123378</td>\n",
       "      <td>10076</td>\n",
       "      <td>133454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>less_median</th>\n",
       "      <td>159308</td>\n",
       "      <td>14749</td>\n",
       "      <td>174057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>282686</td>\n",
       "      <td>24825</td>\n",
       "      <td>307511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "TARGET                        0      1     All\n",
       "HOUR_APPR_PROCESS_START                       \n",
       "greater_median           123378  10076  133454\n",
       "less_median              159308  14749  174057\n",
       "All                      282686  24825  307511"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bad target rate for 'greater median' clients: 0.076\n",
      "bad target rate for 'less median' clients: 0.085\n",
      "greater values of feature is better\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>TARGET</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>greater_median</th>\n",
       "      <td>64114</td>\n",
       "      <td>4537</td>\n",
       "      <td>68651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>less_median</th>\n",
       "      <td>218572</td>\n",
       "      <td>20288</td>\n",
       "      <td>238860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>282686</td>\n",
       "      <td>24825</td>\n",
       "      <td>307511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "TARGET                   0      1     All\n",
       "NONLIVINGAREA_MODE                       \n",
       "greater_median       64114   4537   68651\n",
       "less_median         218572  20288  238860\n",
       "All                 282686  24825  307511"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bad target rate for 'greater median' clients: 0.066\n",
      "bad target rate for 'less median' clients: 0.085\n",
      "greater values of feature is better\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>TARGET</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>greater_median</th>\n",
       "      <td>71823</td>\n",
       "      <td>4703</td>\n",
       "      <td>76526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>less_median</th>\n",
       "      <td>210863</td>\n",
       "      <td>20122</td>\n",
       "      <td>230985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>282686</td>\n",
       "      <td>24825</td>\n",
       "      <td>307511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "TARGET                0      1     All\n",
       "LIVINGAREA_MEDI                       \n",
       "greater_median    71823   4703   76526\n",
       "less_median      210863  20122  230985\n",
       "All              282686  24825  307511"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bad target rate for 'greater median' clients: 0.061\n",
      "bad target rate for 'less median' clients: 0.087\n",
      "greater values of feature is better\n"
     ]
    },
    {
     "data": {
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       "      <th>greater_median</th>\n",
       "      <td>139900</td>\n",
       "      <td>13789</td>\n",
       "      <td>153689</td>\n",
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       "    <tr>\n",
       "      <th>less_median</th>\n",
       "      <td>142786</td>\n",
       "      <td>11036</td>\n",
       "      <td>153822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>282686</td>\n",
       "      <td>24825</td>\n",
       "      <td>307511</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "TARGET               0      1     All\n",
       "DAYS_EMPLOYED                        \n",
       "greater_median  139900  13789  153689\n",
       "less_median     142786  11036  153822\n",
       "All             282686  24825  307511"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bad target rate for 'greater median' clients: 0.09\n",
      "bad target rate for 'less median' clients: 0.072\n",
      "greater values of feature is worse\n"
     ]
    },
    {
     "data": {
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       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>greater_median</th>\n",
       "      <td>73400</td>\n",
       "      <td>5044</td>\n",
       "      <td>78444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>less_median</th>\n",
       "      <td>209286</td>\n",
       "      <td>19781</td>\n",
       "      <td>229067</td>\n",
       "    </tr>\n",
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       "      <th>All</th>\n",
       "      <td>282686</td>\n",
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      "text/plain": [
       "TARGET                             0      1     All\n",
       "YEARS_BEGINEXPLUATATION_MEDI                       \n",
       "greater_median                 73400   5044   78444\n",
       "less_median                   209286  19781  229067\n",
       "All                           282686  24825  307511"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bad target rate for 'greater median' clients: 0.064\n",
      "bad target rate for 'less median' clients: 0.086\n",
      "greater values of feature is better\n"
     ]
    },
    {
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       "TARGET                       0      1     All\n",
       "DAYS_LAST_PHONE_CHANGE                       \n",
       "greater_median          139154  14539  153693\n",
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       "All                     282686  24825  307511"
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     "text": [
      "bad target rate for 'greater median' clients: 0.095\n",
      "bad target rate for 'less median' clients: 0.067\n",
      "greater values of feature is worse\n"
     ]
    },
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       "TARGET                         0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.069\n",
      "bad target rate for 'less median' clients: 0.082\n",
      "greater values of feature is better\n"
     ]
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     "text": [
      "bad target rate for 'greater median' clients: 0.062\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.063\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.057\n",
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      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.082\n",
      "bad target rate for 'less median' clients: 0.08\n",
      "greater values of feature is worse\n"
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      "bad target rate for 'greater median' clients: 0.057\n",
      "bad target rate for 'less median' clients: 0.086\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.043\n",
      "bad target rate for 'less median' clients: 0.091\n",
      "greater values of feature is better\n"
     ]
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       "TARGET                         0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.068\n",
      "bad target rate for 'less median' clients: 0.083\n",
      "greater values of feature is better\n"
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       "      <td>49475</td>\n",
       "      <td>3311</td>\n",
       "      <td>52786</td>\n",
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       "      <td>21514</td>\n",
       "      <td>254725</td>\n",
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       "      <td>282686</td>\n",
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       "      <td>307511</td>\n",
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      ],
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       "TARGET               0      1     All\n",
       "ENTRANCES_MEDI                       \n",
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       "All             282686  24825  307511"
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     "text": [
      "bad target rate for 'greater median' clients: 0.063\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
     ]
    },
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     "text": [
      "bad target rate for 'greater median' clients: 0.063\n",
      "bad target rate for 'less median' clients: 0.086\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.066\n",
      "bad target rate for 'less median' clients: 0.083\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.066\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
     ]
    },
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     "text": [
      "bad target rate for 'greater median' clients: 0.063\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.057\n",
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      "greater values of feature is better\n"
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      "bad target rate for 'greater median' clients: 0.057\n",
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      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.075\n",
      "bad target rate for 'less median' clients: 0.087\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.083\n",
      "bad target rate for 'less median' clients: 0.079\n",
      "greater values of feature is worse\n"
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       "TARGET                        0      1     All\n",
       "NONLIVINGAPARTMENTS_AVG                       \n",
       "greater_median            36776   2672   39448\n",
       "less_median              245910  22153  268063\n",
       "All                      282686  24825  307511"
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     "text": [
      "bad target rate for 'greater median' clients: 0.068\n",
      "bad target rate for 'less median' clients: 0.083\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.065\n",
      "bad target rate for 'less median' clients: 0.083\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.064\n",
      "bad target rate for 'less median' clients: 0.086\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.072\n",
      "bad target rate for 'less median' clients: 0.082\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.064\n",
      "bad target rate for 'less median' clients: 0.085\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.058\n",
      "bad target rate for 'less median' clients: 0.086\n",
      "greater values of feature is better\n"
     ]
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     "text": [
      "bad target rate for 'greater median' clients: 0.098\n",
      "bad target rate for 'less median' clients: 0.063\n",
      "greater values of feature is worse\n"
     ]
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     "text": [
      "bad target rate for 'greater median' clients: 0.066\n",
      "bad target rate for 'less median' clients: 0.085\n",
      "greater values of feature is better\n"
     ]
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     "text": [
      "bad target rate for 'greater median' clients: 0.089\n",
      "bad target rate for 'less median' clients: 0.072\n",
      "greater values of feature is worse\n"
     ]
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       "TARGET               0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.087\n",
      "bad target rate for 'less median' clients: 0.08\n",
      "greater values of feature is worse\n"
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       "      <th>greater_median</th>\n",
       "      <td>30259</td>\n",
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       "      <td>275440</td>\n",
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       "      <td>282686</td>\n",
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       "      <td>307511</td>\n",
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       "TARGET               0      1     All\n",
       "FLOORSMIN_MEDI                       \n",
       "greater_median   30259   1812   32071\n",
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       "All             282686  24825  307511"
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     "text": [
      "bad target rate for 'greater median' clients: 0.056\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
     ]
    },
    {
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       "TARGET                 0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.064\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
     ]
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     "text": [
      "bad target rate for 'greater median' clients: 0.063\n",
      "bad target rate for 'less median' clients: 0.087\n",
      "greater values of feature is better\n"
     ]
    },
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       "TARGET               0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.058\n",
      "bad target rate for 'less median' clients: 0.086\n",
      "greater values of feature is better\n"
     ]
    },
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       "TARGET                           0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.081\n",
      "bad target rate for 'less median' clients: 0.081\n",
      "greater values of feature is better\n"
     ]
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       "TARGET               0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.066\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
     ]
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     "text": [
      "bad target rate for 'greater median' clients: 0.066\n",
      "bad target rate for 'less median' clients: 0.094\n",
      "greater values of feature is better\n"
     ]
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       "TARGET               0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.063\n",
      "bad target rate for 'less median' clients: 0.087\n",
      "greater values of feature is better\n"
     ]
    },
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       "TARGET                         0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.083\n",
      "bad target rate for 'less median' clients: 0.079\n",
      "greater values of feature is worse\n"
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>greater_median</th>\n",
       "      <td>47258</td>\n",
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       "      <td>50505</td>\n",
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       "      <th>less_median</th>\n",
       "      <td>235428</td>\n",
       "      <td>21578</td>\n",
       "      <td>257006</td>\n",
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       "      <th>All</th>\n",
       "      <td>282686</td>\n",
       "      <td>24825</td>\n",
       "      <td>307511</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "TARGET                0      1     All\n",
       "YEARS_BUILD_AVG                       \n",
       "greater_median    47258   3247   50505\n",
       "less_median      235428  21578  257006\n",
       "All              282686  24825  307511"
      ]
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     "text": [
      "bad target rate for 'greater median' clients: 0.064\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
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    },
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     "text": [
      "bad target rate for 'greater median' clients: 0.077\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.062\n",
      "bad target rate for 'less median' clients: 0.087\n",
      "greater values of feature is better\n"
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    },
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       "TARGET               0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.042\n",
      "bad target rate for 'less median' clients: 0.106\n",
      "greater values of feature is better\n"
     ]
    },
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     "text": [
      "bad target rate for 'greater median' clients: 0.064\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.062\n",
      "bad target rate for 'less median' clients: 0.087\n",
      "greater values of feature is better\n"
     ]
    },
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     "text": [
      "bad target rate for 'greater median' clients: 0.066\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
     ]
    },
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     "text": [
      "bad target rate for 'greater median' clients: 0.077\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.057\n",
      "bad target rate for 'less median' clients: 0.083\n",
      "greater values of feature is better\n"
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       "TARGET               0      1     All\n",
       "LIVINGAREA_AVG                       \n",
       "greater_median   71824   4706   76530\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.061\n",
      "bad target rate for 'less median' clients: 0.087\n",
      "greater values of feature is better\n"
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      "bad target rate for 'greater median' clients: 0.064\n",
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      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.095\n",
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      "greater values of feature is worse\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.108\n",
      "bad target rate for 'less median' clients: 0.078\n",
      "greater values of feature is worse\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.049\n",
      "bad target rate for 'less median' clients: 0.112\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.089\n",
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      "greater values of feature is worse\n"
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      "bad target rate for 'greater median' clients: 0.093\n",
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      "greater values of feature is worse\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.065\n",
      "bad target rate for 'less median' clients: 0.083\n",
      "greater values of feature is better\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.088\n",
      "bad target rate for 'less median' clients: 0.078\n",
      "greater values of feature is worse\n"
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     "text": [
      "bad target rate for 'greater median' clients: 0.074\n",
      "bad target rate for 'less median' clients: 0.082\n",
      "greater values of feature is better\n"
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       "TARGET               0      1     All\n",
       "ENTRANCES_AVG                        \n",
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       "All             282686  24825  307511"
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     "text": [
      "bad target rate for 'greater median' clients: 0.063\n",
      "bad target rate for 'less median' clients: 0.085\n",
      "greater values of feature is better\n"
     ]
    },
    {
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       "TARGET               0      1     All\n",
       "ENTRANCES_MODE                       \n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.063\n",
      "bad target rate for 'less median' clients: 0.084\n",
      "greater values of feature is better\n"
     ]
    },
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       "TARGET               0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.082\n",
      "bad target rate for 'less median' clients: 0.079\n",
      "greater values of feature is worse\n"
     ]
    },
    {
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       "TARGET                         0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.105\n",
      "bad target rate for 'less median' clients: 0.078\n",
      "greater values of feature is worse\n"
     ]
    },
    {
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       "TARGET                  0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.066\n",
      "bad target rate for 'less median' clients: 0.085\n",
      "greater values of feature is better\n"
     ]
    },
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       "TARGET               0      1     All\n",
       "FLOORSMAX_MEDI                       \n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.057\n",
      "bad target rate for 'less median' clients: 0.086\n",
      "greater values of feature is better\n"
     ]
    },
    {
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       "TARGET                           0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.078\n",
      "bad target rate for 'less median' clients: 0.081\n",
      "greater values of feature is better\n"
     ]
    },
    {
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       "TARGET                   0      1     All\n",
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     "text": [
      "bad target rate for 'greater median' clients: 0.066\n",
      "bad target rate for 'less median' clients: 0.085\n",
      "greater values of feature is better\n"
     ]
    },
    {
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      "text/plain": [
       "TARGET                 0      1     All\n",
       "BASEMENTAREA_AVG                       \n",
       "greater_median     59643   4096   63739\n",
       "less_median       223043  20729  243772\n",
       "All               282686  24825  307511"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bad target rate for 'greater median' clients: 0.064\n",
      "bad target rate for 'less median' clients: 0.085\n",
      "greater values of feature is better\n"
     ]
    }
   ],
   "source": [
    "# check if greater value of feature leads to better target 0\n",
    "\n",
    "greater_feature_is_better = []\n",
    "greater_feature_is_worse = []\n",
    "\n",
    "\n",
    "for column in df_application_train_numerical.columns[1:]:  # exclude target\n",
    "    \n",
    "    numerical_feature = df_application_train_numerical[column]\n",
    "    numerical_feature_median = numerical_feature.median()\n",
    "    \n",
    "    numerical_feature_to_binary = (numerical_feature > numerical_feature_median)\n",
    "    numerical_feature_to_binary.replace(\n",
    "        to_replace={\n",
    "            True: \"greater_median\",\n",
    "            False: \"less_median\",\n",
    "        },\n",
    "        inplace=True,\n",
    "    )\n",
    "    \n",
    "    target_crosstab = pd.crosstab(\n",
    "        index=numerical_feature_to_binary,\n",
    "        columns=df_application_train_numerical['TARGET'],\n",
    "        margins=True,\n",
    "    )\n",
    "    display(target_crosstab)\n",
    "    \n",
    "    greater_median_1_target_rate = target_crosstab[1][\"greater_median\"] / target_crosstab.loc[\"greater_median\", [0, 1]].sum()\n",
    "    less_median_1_target_rate = target_crosstab[1][\"less_median\"] / target_crosstab.loc[\"less_median\", [0, 1]].sum()\n",
    "    \n",
    "    print(f\"bad target rate for 'greater median' clients: {round(greater_median_1_target_rate, 3)}\")\n",
    "    print(f\"bad target rate for 'less median' clients: {round(less_median_1_target_rate, 3)}\")\n",
    "    \n",
    "    if greater_median_1_target_rate > less_median_1_target_rate:\n",
    "        print(\"greater values of feature is worse\")\n",
    "        greater_feature_is_worse.append(column)\n",
    "    else:\n",
    "        print(\"greater values of feature is better\")\n",
    "        greater_feature_is_better.append(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HOUR_APPR_PROCESS_START',\n",
       " 'NONLIVINGAREA_MODE',\n",
       " 'LIVINGAREA_MEDI',\n",
       " 'YEARS_BEGINEXPLUATATION_MEDI',\n",
       " 'NONLIVINGAPARTMENTS_MODE',\n",
       " 'LIVINGAPARTMENTS_AVG',\n",
       " 'LIVINGAPARTMENTS_MODE',\n",
       " 'FLOORSMIN_AVG',\n",
       " 'FLOORSMAX_MODE',\n",
       " 'EXT_SOURCE_1',\n",
       " 'NONLIVINGAPARTMENTS_MEDI',\n",
       " 'ENTRANCES_MEDI',\n",
       " 'APARTMENTS_MODE',\n",
       " 'COMMONAREA_MODE',\n",
       " 'LANDAREA_MODE',\n",
       " 'LIVINGAPARTMENTS_MEDI',\n",
       " 'ELEVATORS_MEDI',\n",
       " 'ELEVATORS_MODE',\n",
       " 'REGION_POPULATION_RELATIVE',\n",
       " 'NONLIVINGAPARTMENTS_AVG',\n",
       " 'COMMONAREA_AVG',\n",
       " 'YEARS_BEGINEXPLUATATION_AVG',\n",
       " 'AMT_REQ_CREDIT_BUREAU_QRT',\n",
       " 'BASEMENTAREA_MEDI',\n",
       " 'FLOORSMAX_AVG',\n",
       " 'BASEMENTAREA_MODE',\n",
       " 'FLOORSMIN_MEDI',\n",
       " 'YEARS_BUILD_MEDI',\n",
       " 'APARTMENTS_MEDI',\n",
       " 'ELEVATORS_AVG',\n",
       " 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
       " 'LANDAREA_AVG',\n",
       " 'AMT_GOODS_PRICE',\n",
       " 'APARTMENTS_AVG',\n",
       " 'YEARS_BUILD_AVG',\n",
       " 'AMT_CREDIT',\n",
       " 'TOTALAREA_MODE',\n",
       " 'EXT_SOURCE_3',\n",
       " 'YEARS_BUILD_MODE',\n",
       " 'LIVINGAREA_MODE',\n",
       " 'LANDAREA_MEDI',\n",
       " 'AMT_INCOME_TOTAL',\n",
       " 'FLOORSMIN_MODE',\n",
       " 'LIVINGAREA_AVG',\n",
       " 'YEARS_BEGINEXPLUATATION_MODE',\n",
       " 'EXT_SOURCE_2',\n",
       " 'COMMONAREA_MEDI',\n",
       " 'AMT_REQ_CREDIT_BUREAU_MON',\n",
       " 'ENTRANCES_AVG',\n",
       " 'ENTRANCES_MODE',\n",
       " 'NONLIVINGAREA_AVG',\n",
       " 'FLOORSMAX_MEDI',\n",
       " 'AMT_REQ_CREDIT_BUREAU_WEEK',\n",
       " 'NONLIVINGAREA_MEDI',\n",
       " 'BASEMENTAREA_AVG']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greater_feature_is_better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DAYS_EMPLOYED',\n",
       " 'DAYS_LAST_PHONE_CHANGE',\n",
       " 'AMT_REQ_CREDIT_BUREAU_YEAR',\n",
       " 'OBS_30_CNT_SOCIAL_CIRCLE',\n",
       " 'DAYS_BIRTH',\n",
       " 'DAYS_REGISTRATION',\n",
       " 'OWN_CAR_AGE',\n",
       " 'OBS_60_CNT_SOCIAL_CIRCLE',\n",
       " 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
       " 'DEF_60_CNT_SOCIAL_CIRCLE',\n",
       " 'CNT_CHILDREN',\n",
       " 'DAYS_ID_PUBLISH',\n",
       " 'CNT_FAM_MEMBERS',\n",
       " 'AMT_ANNUITY',\n",
       " 'DEF_30_CNT_SOCIAL_CIRCLE']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greater_feature_is_worse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "greater_feature_is_better_rank = df_application_train_numerical[greater_feature_is_better].rank(\n",
    "    ascending=False, na_option='bottom'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "greater_feature_is_worse_rank = df_application_train_numerical[greater_feature_is_worse].rank(\n",
    "    ascending=True, na_option='bottom'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranked_data = pd.concat(\n",
    "    [greater_feature_is_better_rank, greater_feature_is_worse_rank],\n",
    "    axis=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>HOUR_APPR_PROCESS_START</th>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <th>FLOORSMIN_AVG</th>\n",
       "      <th>FLOORSMAX_MODE</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>...</th>\n",
       "      <th>DAYS_REGISTRATION</th>\n",
       "      <th>OWN_CAR_AGE</th>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>DAYS_ID_PUBLISH</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>223777.5</td>\n",
       "      <td>104266.5</td>\n",
       "      <td>133168.5</td>\n",
       "      <td>142641.0</td>\n",
       "      <td>64370.0</td>\n",
       "      <td>87372.5</td>\n",
       "      <td>86564.0</td>\n",
       "      <td>71572.5</td>\n",
       "      <td>132068.0</td>\n",
       "      <td>132834.0</td>\n",
       "      <td>...</td>\n",
       "      <td>181371.0</td>\n",
       "      <td>206047.0</td>\n",
       "      <td>228419.5</td>\n",
       "      <td>132252.0</td>\n",
       "      <td>304147.5</td>\n",
       "      <td>107686.0</td>\n",
       "      <td>211770.0</td>\n",
       "      <td>33924.0</td>\n",
       "      <td>151706.0</td>\n",
       "      <td>302314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>186302.0</td>\n",
       "      <td>104266.5</td>\n",
       "      <td>101397.0</td>\n",
       "      <td>53576.5</td>\n",
       "      <td>64370.0</td>\n",
       "      <td>45743.5</td>\n",
       "      <td>47843.0</td>\n",
       "      <td>29622.5</td>\n",
       "      <td>53526.0</td>\n",
       "      <td>104694.0</td>\n",
       "      <td>...</td>\n",
       "      <td>256624.0</td>\n",
       "      <td>206047.0</td>\n",
       "      <td>189101.5</td>\n",
       "      <td>132252.0</td>\n",
       "      <td>140361.0</td>\n",
       "      <td>107686.0</td>\n",
       "      <td>295553.0</td>\n",
       "      <td>147026.0</td>\n",
       "      <td>236262.5</td>\n",
       "      <td>135662.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>256330.5</td>\n",
       "      <td>222670.5</td>\n",
       "      <td>230336.5</td>\n",
       "      <td>232508.0</td>\n",
       "      <td>200754.5</td>\n",
       "      <td>202412.0</td>\n",
       "      <td>202412.0</td>\n",
       "      <td>203190.5</td>\n",
       "      <td>231001.5</td>\n",
       "      <td>220822.5</td>\n",
       "      <td>...</td>\n",
       "      <td>162146.5</td>\n",
       "      <td>97612.5</td>\n",
       "      <td>82333.5</td>\n",
       "      <td>132252.0</td>\n",
       "      <td>140361.0</td>\n",
       "      <td>107686.0</td>\n",
       "      <td>191436.0</td>\n",
       "      <td>33924.0</td>\n",
       "      <td>5213.0</td>\n",
       "      <td>135662.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100006</th>\n",
       "      <td>22139.5</td>\n",
       "      <td>222670.5</td>\n",
       "      <td>230336.5</td>\n",
       "      <td>232508.0</td>\n",
       "      <td>200754.5</td>\n",
       "      <td>202412.0</td>\n",
       "      <td>202412.0</td>\n",
       "      <td>203190.5</td>\n",
       "      <td>231001.5</td>\n",
       "      <td>220822.5</td>\n",
       "      <td>...</td>\n",
       "      <td>32255.0</td>\n",
       "      <td>206047.0</td>\n",
       "      <td>228419.5</td>\n",
       "      <td>286752.0</td>\n",
       "      <td>140361.0</td>\n",
       "      <td>107686.0</td>\n",
       "      <td>196224.0</td>\n",
       "      <td>147026.0</td>\n",
       "      <td>195846.5</td>\n",
       "      <td>135662.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100007</th>\n",
       "      <td>186302.0</td>\n",
       "      <td>222670.5</td>\n",
       "      <td>230336.5</td>\n",
       "      <td>232508.0</td>\n",
       "      <td>200754.5</td>\n",
       "      <td>202412.0</td>\n",
       "      <td>202412.0</td>\n",
       "      <td>203190.5</td>\n",
       "      <td>231001.5</td>\n",
       "      <td>220822.5</td>\n",
       "      <td>...</td>\n",
       "      <td>160353.0</td>\n",
       "      <td>206047.0</td>\n",
       "      <td>82333.5</td>\n",
       "      <td>132252.0</td>\n",
       "      <td>140361.0</td>\n",
       "      <td>107686.0</td>\n",
       "      <td>142808.0</td>\n",
       "      <td>33924.0</td>\n",
       "      <td>122879.0</td>\n",
       "      <td>135662.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            HOUR_APPR_PROCESS_START  NONLIVINGAREA_MODE  LIVINGAREA_MEDI  \\\n",
       "SK_ID_CURR                                                                 \n",
       "100002                     223777.5            104266.5         133168.5   \n",
       "100003                     186302.0            104266.5         101397.0   \n",
       "100004                     256330.5            222670.5         230336.5   \n",
       "100006                      22139.5            222670.5         230336.5   \n",
       "100007                     186302.0            222670.5         230336.5   \n",
       "\n",
       "            YEARS_BEGINEXPLUATATION_MEDI  NONLIVINGAPARTMENTS_MODE  \\\n",
       "SK_ID_CURR                                                           \n",
       "100002                          142641.0                   64370.0   \n",
       "100003                           53576.5                   64370.0   \n",
       "100004                          232508.0                  200754.5   \n",
       "100006                          232508.0                  200754.5   \n",
       "100007                          232508.0                  200754.5   \n",
       "\n",
       "            LIVINGAPARTMENTS_AVG  LIVINGAPARTMENTS_MODE  FLOORSMIN_AVG  \\\n",
       "SK_ID_CURR                                                               \n",
       "100002                   87372.5                86564.0        71572.5   \n",
       "100003                   45743.5                47843.0        29622.5   \n",
       "100004                  202412.0               202412.0       203190.5   \n",
       "100006                  202412.0               202412.0       203190.5   \n",
       "100007                  202412.0               202412.0       203190.5   \n",
       "\n",
       "            FLOORSMAX_MODE  EXT_SOURCE_1  ...  DAYS_REGISTRATION  OWN_CAR_AGE  \\\n",
       "SK_ID_CURR                                ...                                   \n",
       "100002            132068.0      132834.0  ...           181371.0     206047.0   \n",
       "100003             53526.0      104694.0  ...           256624.0     206047.0   \n",
       "100004            231001.5      220822.5  ...           162146.5      97612.5   \n",
       "100006            231001.5      220822.5  ...            32255.0     206047.0   \n",
       "100007            231001.5      220822.5  ...           160353.0     206047.0   \n",
       "\n",
       "            OBS_60_CNT_SOCIAL_CIRCLE  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "SK_ID_CURR                                                        \n",
       "100002                      228419.5                   132252.0   \n",
       "100003                      189101.5                   132252.0   \n",
       "100004                       82333.5                   132252.0   \n",
       "100006                      228419.5                   286752.0   \n",
       "100007                       82333.5                   132252.0   \n",
       "\n",
       "            DEF_60_CNT_SOCIAL_CIRCLE  CNT_CHILDREN  DAYS_ID_PUBLISH  \\\n",
       "SK_ID_CURR                                                            \n",
       "100002                      304147.5      107686.0         211770.0   \n",
       "100003                      140361.0      107686.0         295553.0   \n",
       "100004                      140361.0      107686.0         191436.0   \n",
       "100006                      140361.0      107686.0         196224.0   \n",
       "100007                      140361.0      107686.0         142808.0   \n",
       "\n",
       "            CNT_FAM_MEMBERS  AMT_ANNUITY  DEF_30_CNT_SOCIAL_CIRCLE  \n",
       "SK_ID_CURR                                                          \n",
       "100002              33924.0     151706.0                  302314.0  \n",
       "100003             147026.0     236262.5                  135662.5  \n",
       "100004              33924.0       5213.0                  135662.5  \n",
       "100006             147026.0     195846.5                  135662.5  \n",
       "100007              33924.0     122879.0                  135662.5  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranked_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranked_data_mean = pd.DataFrame(ranked_data.mean(axis=1), columns=['rank'])\n",
    "ranked_data_mean['TARGET'] = df_application_train_numerical['TARGET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>rank</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>131987.300000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>98595.014286</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>191589.842857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100006</th>\n",
       "      <td>207163.014286</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100007</th>\n",
       "      <td>186950.014286</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     rank  TARGET\n",
       "SK_ID_CURR                       \n",
       "100002      131987.300000       1\n",
       "100003       98595.014286       0\n",
       "100004      191589.842857       0\n",
       "100006      207163.014286       0\n",
       "100007      186950.014286       0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranked_data_mean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rank_target_0 = ranked_data_mean[ranked_data_mean['TARGET'] == 0]['rank']  # GOOD\n",
    "rank_target_1 = ranked_data_mean[ranked_data_mean['TARGET'] == 1]['rank']  # BAD\n",
    "\n",
    "plt.scatter(rank_target_0, len(rank_target_0) * [0], color='lightgreen')\n",
    "plt.scatter(rank_target_1, len(rank_target_1) * [0], color='red');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    ranked_data_mean['rank'],\n",
    "    ranked_data_mean['TARGET'],\n",
    "    color='blue',\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### hotelling test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.0\n"
     ]
    }
   ],
   "source": [
    "_, _, p_value = hotelling_t2_1samp_test(\n",
    "    X=df_application_test_numerical.dropna(),\n",
    "    mu=df_application_test_numerical.mean(axis=0),\n",
    ")\n",
    "print(f\"p-value: {round(p_value, 3)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.0\n"
     ]
    }
   ],
   "source": [
    "_, _, p_value = hotelling_t2_2samp_test(\n",
    "    X=df_application_train_numerical.loc[:, df_application_train_numerical.columns != 'TARGET'].dropna(),\n",
    "    Y=df_application_test_numerical.dropna(),\n",
    ")\n",
    "print(f\"p-value: {round(p_value, 3)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mahalanobis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_application_train_numerical.drop([\"TARGET\"], axis=1, inplace=True)\n",
    "df_application_train_numerical.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 11351/11351 [00:11<00:00, 983.49it/s]\n"
     ]
    }
   ],
   "source": [
    "distance_vector, t2_statistic_vector, f_statistic_vector, p_value_vector = mahalanobis_outlier_test(\n",
    "    df_application_train_numerical,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "mahalanobis_df = pd.DataFrame(\n",
    "    {\n",
    "        \"distance\": distance_vector,\n",
    "        \"t2_statistic\": t2_statistic_vector,\n",
    "        \"f_statistic\": f_statistic_vector,\n",
    "        \"p_value\": p_value_vector,\n",
    "    },\n",
    "    index=df_application_train_numerical.index,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>t2_statistic</th>\n",
       "      <th>f_statistic</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100083</th>\n",
       "      <td>5.893177</td>\n",
       "      <td>5.892658</td>\n",
       "      <td>5.856834</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100145</th>\n",
       "      <td>4.852237</td>\n",
       "      <td>4.851809</td>\n",
       "      <td>4.822314</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100165</th>\n",
       "      <td>5.655791</td>\n",
       "      <td>5.655292</td>\n",
       "      <td>5.620912</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100179</th>\n",
       "      <td>14.617473</td>\n",
       "      <td>14.616185</td>\n",
       "      <td>14.527329</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100190</th>\n",
       "      <td>6.745754</td>\n",
       "      <td>6.745160</td>\n",
       "      <td>6.704154</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             distance  t2_statistic  f_statistic       p_value\n",
       "SK_ID_CURR                                                    \n",
       "100083       5.893177      5.892658     5.856834  1.110223e-16\n",
       "100145       4.852237      4.851809     4.822314  1.110223e-16\n",
       "100165       5.655791      5.655292     5.620912  1.110223e-16\n",
       "100179      14.617473     14.616185    14.527329  1.110223e-16\n",
       "100190       6.745754      6.745160     6.704154  1.110223e-16"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mahalanobis_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>distance</th>\n",
       "      <th>t2_statistic</th>\n",
       "      <th>f_statistic</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>267087</th>\n",
       "      <td>96.176529</td>\n",
       "      <td>96.168056</td>\n",
       "      <td>95.583422</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375912</th>\n",
       "      <td>93.954792</td>\n",
       "      <td>93.946515</td>\n",
       "      <td>93.375386</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431402</th>\n",
       "      <td>88.007551</td>\n",
       "      <td>87.999798</td>\n",
       "      <td>87.464821</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342317</th>\n",
       "      <td>80.687618</td>\n",
       "      <td>80.680510</td>\n",
       "      <td>80.190029</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113207</th>\n",
       "      <td>75.622255</td>\n",
       "      <td>75.615593</td>\n",
       "      <td>75.155904</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353955</th>\n",
       "      <td>2.726828</td>\n",
       "      <td>2.726587</td>\n",
       "      <td>2.710012</td>\n",
       "      <td>7.710499e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268603</th>\n",
       "      <td>2.670335</td>\n",
       "      <td>2.670100</td>\n",
       "      <td>2.653868</td>\n",
       "      <td>2.675304e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250280</th>\n",
       "      <td>2.634803</td>\n",
       "      <td>2.634571</td>\n",
       "      <td>2.618554</td>\n",
       "      <td>5.809131e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377361</th>\n",
       "      <td>2.596109</td>\n",
       "      <td>2.595880</td>\n",
       "      <td>2.580099</td>\n",
       "      <td>1.342815e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250087</th>\n",
       "      <td>2.554635</td>\n",
       "      <td>2.554410</td>\n",
       "      <td>2.538881</td>\n",
       "      <td>3.271272e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11351 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             distance  t2_statistic  f_statistic       p_value\n",
       "SK_ID_CURR                                                    \n",
       "267087      96.176529     96.168056    95.583422  1.110223e-16\n",
       "375912      93.954792     93.946515    93.375386  1.110223e-16\n",
       "431402      88.007551     87.999798    87.464821  1.110223e-16\n",
       "342317      80.687618     80.680510    80.190029  1.110223e-16\n",
       "113207      75.622255     75.615593    75.155904  1.110223e-16\n",
       "...               ...           ...          ...           ...\n",
       "353955       2.726828      2.726587     2.710012  7.710499e-13\n",
       "268603       2.670335      2.670100     2.653868  2.675304e-12\n",
       "250280       2.634803      2.634571     2.618554  5.809131e-12\n",
       "377361       2.596109      2.595880     2.580099  1.342815e-11\n",
       "250087       2.554635      2.554410     2.538881  3.271272e-11\n",
       "\n",
       "[11351 rows x 4 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mahalanobis_df.sort_values(by=[\"distance\"], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_application_train_pca = pca.fit_transform(df_application_train_numerical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(pca.explained_variance_)\n",
    "plt.plot(pca.explained_variance_, 'o')\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(pca.explained_variance_ratio_)\n",
    "plt.plot(pca.explained_variance_ratio_, 'o')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}