task-3-project/SystemD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0eb85563",
   "metadata": {},
   "source": [
    "## Importing Necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a9801927",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from lightgbm import LGBMClassifier\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe80454",
   "metadata": {},
   "source": [
    "## Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "47a000cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train_data.csv\")\n",
    "test = pd.read_csv(\"test_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c7ce6125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>450407</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>227520.0</td>\n",
       "      <td>11065.5</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>271298</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>247500.0</td>\n",
       "      <td>1882372.5</td>\n",
       "      <td>65560.5</td>\n",
       "      <td>1719000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>122238</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>101880.0</td>\n",
       "      <td>10827.0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>305311</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>81000.0</td>\n",
       "      <td>405000.0</td>\n",
       "      <td>20677.5</td>\n",
       "      <td>405000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>414121</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>888840.0</td>\n",
       "      <td>29506.5</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  \\\n",
       "0      450407         Cash loans           F            N               Y   \n",
       "1      271298         Cash loans           M            Y               Y   \n",
       "2      122238         Cash loans           M            Y               Y   \n",
       "3      305311         Cash loans           M            N               N   \n",
       "4      414121         Cash loans           F            N               Y   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0             1           67500.0    227520.0      11065.5         180000.0   \n",
       "1             1          247500.0   1882372.5      65560.5        1719000.0   \n",
       "2             1          180000.0    101880.0      10827.0          90000.0   \n",
       "3             0           81000.0    405000.0      20677.5         405000.0   \n",
       "4             0          157500.0    888840.0      29506.5         675000.0   \n",
       "\n",
       "   ... FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0  ...                0                0                0   \n",
       "1  ...                0                0                0   \n",
       "2  ...                0                0                0   \n",
       "3  ...                0                0                0   \n",
       "4  ...                0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        0.0                       0.0   \n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        0.0                       0.0   \n",
       "4                        0.0                       0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        1.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        2.0   \n",
       "3                         0.0                        0.0   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  TARGET  \n",
       "0                        0.0                         2.0       0  \n",
       "1                        1.0                         3.0       0  \n",
       "2                        0.0                         1.0       0  \n",
       "3                        0.0                         2.0       0  \n",
       "4                        0.0                         2.0       0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5dea2a0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>367294</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>265306.5</td>\n",
       "      <td>25317.0</td>\n",
       "      <td>252000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>439847</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>346500.0</td>\n",
       "      <td>21069.0</td>\n",
       "      <td>346500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>380562</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>360000.0</td>\n",
       "      <td>545040.0</td>\n",
       "      <td>36553.5</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>407238</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>307557.0</td>\n",
       "      <td>20682.0</td>\n",
       "      <td>265500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>239910</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>1056447.0</td>\n",
       "      <td>31018.5</td>\n",
       "      <td>922500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  \\\n",
       "0      367294         Cash loans           F            N               Y   \n",
       "1      439847         Cash loans           F            N               Y   \n",
       "2      380562         Cash loans           M            Y               N   \n",
       "3      407238         Cash loans           F            N               Y   \n",
       "4      239910         Cash loans           F            N               Y   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0             0          180000.0    265306.5      25317.0         252000.0   \n",
       "1             0          202500.0    346500.0      21069.0         346500.0   \n",
       "2             0          360000.0    545040.0      36553.5         450000.0   \n",
       "3             0          135000.0    307557.0      20682.0         265500.0   \n",
       "4             0          157500.0   1056447.0      31018.5         922500.0   \n",
       "\n",
       "   ... FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0  ...                0                0                0                0   \n",
       "1  ...                0                0                0                0   \n",
       "2  ...                0                0                0                0   \n",
       "3  ...                0                0                0                0   \n",
       "4  ...                0                0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "3                        0.0                        0.0   \n",
       "4                        NaN                        NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        1.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         1.0                        0.0   \n",
       "4                         NaN                        NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        2.0                         5.0  \n",
       "1                        0.0                         5.0  \n",
       "2                        1.0                         5.0  \n",
       "3                        1.0                         2.0  \n",
       "4                        NaN                         NaN  \n",
       "\n",
       "[5 rows x 121 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd7b1504",
   "metadata": {},
   "source": [
    "## Removing Columns having null values greater than 60%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "901a7da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train.dropna(axis = 1, thresh = 0.4 * (len(train)))\n",
    "test_data = test.dropna(axis = 1, thresh = 0.4 * (len(test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3100bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(184506, 105)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fd5332c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(123005, 104)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "68ebf301",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train_data['TARGET']\n",
    "train_data = train_data.drop('TARGET', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "315c7f32",
   "metadata": {},
   "source": [
    "## Detecting and Replacing outliers (Flooring and Caping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bca94199",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = train_data.quantile(0.25)\n",
    "Q3 = train_data.quantile(0.75)\n",
    "IQR = Q3 - Q1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e665f70a",
   "metadata": {},
   "source": [
    "### Detecting train_data outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4acf4b6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK_ID_CURR : 0\n",
      "\n",
      "CNT_CHILDREN : 2502\n",
      "\n",
      "AMT_INCOME_TOTAL : 8393\n",
      "\n",
      "AMT_CREDIT : 3936\n",
      "\n",
      "AMT_ANNUITY : 4464\n",
      "\n",
      "AMT_GOODS_PRICE : 8914\n",
      "\n",
      "REGION_POPULATION_RELATIVE : 5009\n",
      "\n",
      "DAYS_BIRTH : 0\n",
      "\n",
      "DAYS_EMPLOYED : 43402\n",
      "\n",
      "DAYS_REGISTRATION : 373\n",
      "\n",
      "DAYS_ID_PUBLISH : 0\n",
      "\n",
      "FLAG_MOBIL : 1\n",
      "\n",
      "FLAG_EMP_PHONE : 33239\n",
      "\n",
      "FLAG_WORK_PHONE : 36738\n",
      "\n",
      "FLAG_CONT_MOBILE : 361\n",
      "\n",
      "FLAG_PHONE : 0\n",
      "\n",
      "FLAG_EMAIL : 10450\n",
      "\n",
      "CNT_FAM_MEMBERS : 2360\n",
      "\n",
      "REGION_RATING_CLIENT : 48161\n",
      "\n",
      "REGION_RATING_CLIENT_W_CITY : 46695\n",
      "\n",
      "HOUR_APPR_PROCESS_START : 1346\n",
      "\n",
      "REG_REGION_NOT_LIVE_REGION : 2800\n",
      "\n",
      "REG_REGION_NOT_WORK_REGION : 9465\n",
      "\n",
      "LIVE_REGION_NOT_WORK_REGION : 7597\n",
      "\n",
      "REG_CITY_NOT_LIVE_CITY : 14535\n",
      "\n",
      "REG_CITY_NOT_WORK_CITY : 42496\n",
      "\n",
      "LIVE_CITY_NOT_WORK_CITY : 33074\n",
      "\n",
      "EXT_SOURCE_1 : 0\n",
      "\n",
      "EXT_SOURCE_2 : 0\n",
      "\n",
      "EXT_SOURCE_3 : 0\n",
      "\n",
      "APARTMENTS_AVG : 6312\n",
      "\n",
      "BASEMENTAREA_AVG : 4336\n",
      "\n",
      "YEARS_BEGINEXPLUATATION_AVG : 2897\n",
      "\n",
      "ELEVATORS_AVG : 6168\n",
      "\n",
      "ENTRANCES_AVG : 2326\n",
      "\n",
      "FLOORSMAX_AVG : 3149\n",
      "\n",
      "LANDAREA_AVG : 4140\n",
      "\n",
      "LIVINGAREA_AVG : 7440\n",
      "\n",
      "NONLIVINGAREA_AVG : 10015\n",
      "\n",
      "APARTMENTS_MODE : 6163\n",
      "\n",
      "BASEMENTAREA_MODE : 4138\n",
      "\n",
      "YEARS_BEGINEXPLUATATION_MODE : 2681\n",
      "\n",
      "ELEVATORS_MODE : 5743\n",
      "\n",
      "ENTRANCES_MODE : 2301\n",
      "\n",
      "FLOORSMAX_MODE : 3071\n",
      "\n",
      "LANDAREA_MODE : 4218\n",
      "\n",
      "LIVINGAREA_MODE : 7802\n",
      "\n",
      "NONLIVINGAREA_MODE : 11400\n",
      "\n",
      "APARTMENTS_MEDI : 6351\n",
      "\n",
      "BASEMENTAREA_MEDI : 4338\n",
      "\n",
      "YEARS_BEGINEXPLUATATION_MEDI : 2882\n",
      "\n",
      "ELEVATORS_MEDI : 6130\n",
      "\n",
      "ENTRANCES_MEDI : 2332\n",
      "\n",
      "FLOORSMAX_MEDI : 3232\n",
      "\n",
      "LANDAREA_MEDI : 4131\n",
      "\n",
      "LIVINGAREA_MEDI : 7664\n",
      "\n",
      "NONLIVINGAREA_MEDI : 10470\n",
      "\n",
      "TOTALAREA_MODE : 7230\n",
      "\n",
      "OBS_30_CNT_SOCIAL_CIRCLE : 12104\n",
      "\n",
      "DEF_30_CNT_SOCIAL_CIRCLE : 21174\n",
      "\n",
      "OBS_60_CNT_SOCIAL_CIRCLE : 11862\n",
      "\n",
      "DEF_60_CNT_SOCIAL_CIRCLE : 15519\n",
      "\n",
      "DAYS_LAST_PHONE_CHANGE : 248\n",
      "\n",
      "FLAG_DOCUMENT_2 : 7\n",
      "\n",
      "FLAG_DOCUMENT_3 : 0\n",
      "\n",
      "FLAG_DOCUMENT_4 : 17\n",
      "\n",
      "FLAG_DOCUMENT_5 : 2876\n",
      "\n",
      "FLAG_DOCUMENT_6 : 16299\n",
      "\n",
      "FLAG_DOCUMENT_7 : 33\n",
      "\n",
      "FLAG_DOCUMENT_8 : 15187\n",
      "\n",
      "FLAG_DOCUMENT_9 : 708\n",
      "\n",
      "FLAG_DOCUMENT_10 : 4\n",
      "\n",
      "FLAG_DOCUMENT_11 : 743\n",
      "\n",
      "FLAG_DOCUMENT_12 : 0\n",
      "\n",
      "FLAG_DOCUMENT_13 : 689\n",
      "\n",
      "FLAG_DOCUMENT_14 : 539\n",
      "\n",
      "FLAG_DOCUMENT_15 : 239\n",
      "\n",
      "FLAG_DOCUMENT_16 : 1785\n",
      "\n",
      "FLAG_DOCUMENT_17 : 50\n",
      "\n",
      "FLAG_DOCUMENT_18 : 1452\n",
      "\n",
      "FLAG_DOCUMENT_19 : 116\n",
      "\n",
      "FLAG_DOCUMENT_20 : 102\n",
      "\n",
      "FLAG_DOCUMENT_21 : 60\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_HOUR : 963\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_DAY : 921\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_WEEK : 5114\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_MON : 26109\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_QRT : 30507\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR : 1977\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_cols = train_data.dtypes != object\n",
    "train_cols = train_data.columns[train_cols].tolist()\n",
    "for i in range(len(train_cols)):\n",
    "    q1 = Q1[train_cols[i]]\n",
    "    q3 = Q3[train_cols[i]]\n",
    "    iqr = IQR[train_cols[i]]\n",
    "    outliers = train_data[((train_data[train_cols[i]] < (q1 - 1.5 * iqr)) | (train_data[train_cols[i]] > (q3 + 1.5 * iqr)))]\n",
    "    print(\"{} : {}\\n\".format(train_cols[i], outliers.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be70b7a",
   "metadata": {},
   "source": [
    "### Replacing train_data outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9408e1ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK_ID_CURR : 0\n",
      "\n",
      "CNT_CHILDREN : 0\n",
      "\n",
      "AMT_INCOME_TOTAL : 0\n",
      "\n",
      "AMT_CREDIT : 0\n",
      "\n",
      "AMT_ANNUITY : 0\n",
      "\n",
      "AMT_GOODS_PRICE : 0\n",
      "\n",
      "REGION_POPULATION_RELATIVE : 0\n",
      "\n",
      "DAYS_BIRTH : 0\n",
      "\n",
      "DAYS_EMPLOYED : 0\n",
      "\n",
      "DAYS_REGISTRATION : 0\n",
      "\n",
      "DAYS_ID_PUBLISH : 0\n",
      "\n",
      "FLAG_MOBIL : 0\n",
      "\n",
      "FLAG_EMP_PHONE : 0\n",
      "\n",
      "FLAG_WORK_PHONE : 0\n",
      "\n",
      "FLAG_CONT_MOBILE : 0\n",
      "\n",
      "FLAG_PHONE : 0\n",
      "\n",
      "FLAG_EMAIL : 0\n",
      "\n",
      "CNT_FAM_MEMBERS : 0\n",
      "\n",
      "REGION_RATING_CLIENT : 0\n",
      "\n",
      "REGION_RATING_CLIENT_W_CITY : 0\n",
      "\n",
      "HOUR_APPR_PROCESS_START : 0\n",
      "\n",
      "REG_REGION_NOT_LIVE_REGION : 0\n",
      "\n",
      "REG_REGION_NOT_WORK_REGION : 0\n",
      "\n",
      "LIVE_REGION_NOT_WORK_REGION : 0\n",
      "\n",
      "REG_CITY_NOT_LIVE_CITY : 0\n",
      "\n",
      "REG_CITY_NOT_WORK_CITY : 0\n",
      "\n",
      "LIVE_CITY_NOT_WORK_CITY : 0\n",
      "\n",
      "EXT_SOURCE_1 : 0\n",
      "\n",
      "EXT_SOURCE_2 : 0\n",
      "\n",
      "EXT_SOURCE_3 : 0\n",
      "\n",
      "APARTMENTS_AVG : 0\n",
      "\n",
      "BASEMENTAREA_AVG : 0\n",
      "\n",
      "YEARS_BEGINEXPLUATATION_AVG : 0\n",
      "\n",
      "ELEVATORS_AVG : 0\n",
      "\n",
      "ENTRANCES_AVG : 0\n",
      "\n",
      "FLOORSMAX_AVG : 0\n",
      "\n",
      "LANDAREA_AVG : 0\n",
      "\n",
      "LIVINGAREA_AVG : 0\n",
      "\n",
      "NONLIVINGAREA_AVG : 0\n",
      "\n",
      "APARTMENTS_MODE : 0\n",
      "\n",
      "BASEMENTAREA_MODE : 0\n",
      "\n",
      "YEARS_BEGINEXPLUATATION_MODE : 0\n",
      "\n",
      "ELEVATORS_MODE : 0\n",
      "\n",
      "ENTRANCES_MODE : 0\n",
      "\n",
      "FLOORSMAX_MODE : 0\n",
      "\n",
      "LANDAREA_MODE : 0\n",
      "\n",
      "LIVINGAREA_MODE : 0\n",
      "\n",
      "NONLIVINGAREA_MODE : 0\n",
      "\n",
      "APARTMENTS_MEDI : 0\n",
      "\n",
      "BASEMENTAREA_MEDI : 0\n",
      "\n",
      "YEARS_BEGINEXPLUATATION_MEDI : 0\n",
      "\n",
      "ELEVATORS_MEDI : 0\n",
      "\n",
      "ENTRANCES_MEDI : 0\n",
      "\n",
      "FLOORSMAX_MEDI : 0\n",
      "\n",
      "LANDAREA_MEDI : 0\n",
      "\n",
      "LIVINGAREA_MEDI : 0\n",
      "\n",
      "NONLIVINGAREA_MEDI : 0\n",
      "\n",
      "TOTALAREA_MODE : 0\n",
      "\n",
      "OBS_30_CNT_SOCIAL_CIRCLE : 0\n",
      "\n",
      "DEF_30_CNT_SOCIAL_CIRCLE : 0\n",
      "\n",
      "OBS_60_CNT_SOCIAL_CIRCLE : 0\n",
      "\n",
      "DEF_60_CNT_SOCIAL_CIRCLE : 0\n",
      "\n",
      "DAYS_LAST_PHONE_CHANGE : 0\n",
      "\n",
      "FLAG_DOCUMENT_2 : 0\n",
      "\n",
      "FLAG_DOCUMENT_3 : 0\n",
      "\n",
      "FLAG_DOCUMENT_4 : 0\n",
      "\n",
      "FLAG_DOCUMENT_5 : 0\n",
      "\n",
      "FLAG_DOCUMENT_6 : 0\n",
      "\n",
      "FLAG_DOCUMENT_7 : 0\n",
      "\n",
      "FLAG_DOCUMENT_8 : 0\n",
      "\n",
      "FLAG_DOCUMENT_9 : 0\n",
      "\n",
      "FLAG_DOCUMENT_10 : 0\n",
      "\n",
      "FLAG_DOCUMENT_11 : 0\n",
      "\n",
      "FLAG_DOCUMENT_12 : 0\n",
      "\n",
      "FLAG_DOCUMENT_13 : 0\n",
      "\n",
      "FLAG_DOCUMENT_14 : 0\n",
      "\n",
      "FLAG_DOCUMENT_15 : 0\n",
      "\n",
      "FLAG_DOCUMENT_16 : 0\n",
      "\n",
      "FLAG_DOCUMENT_17 : 0\n",
      "\n",
      "FLAG_DOCUMENT_18 : 0\n",
      "\n",
      "FLAG_DOCUMENT_19 : 0\n",
      "\n",
      "FLAG_DOCUMENT_20 : 0\n",
      "\n",
      "FLAG_DOCUMENT_21 : 0\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_HOUR : 0\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_DAY : 0\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_WEEK : 0\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_MON : 0\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_QRT : 0\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR : 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(train_cols)):\n",
    "    q1 = Q1[train_cols[i]]\n",
    "    q3 = Q3[train_cols[i]]\n",
    "    iqr = IQR[train_cols[i]]\n",
    "    uw = q3 + 1.5 * iqr\n",
    "    lw = q1 - 1.5 * iqr\n",
    "    train_data[train_cols[i]] = np.where(train_data[train_cols[i]] > uw, uw, train_data[train_cols[i]])\n",
    "    train_data[train_cols[i]] = np.where(train_data[train_cols[i]] < lw, lw, train_data[train_cols[i]])\n",
    "    outliers = train_data[((train_data[train_cols[i]] < (q1 - 1.5 * iqr)) | (train_data[train_cols[i]] > (q3 + 1.5 * iqr)))]\n",
    "    print(\"{} : {}\\n\".format(train_cols[i], outliers.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bddfa084",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1_t = test_data.quantile(0.25)\n",
    "Q3_t = test_data.quantile(0.75)\n",
    "IQR_t = Q3_t - Q1_t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdcfe80",
   "metadata": {},
   "source": [
    "### Detecting test_data outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cef61b3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK_ID_CURR : 0\n",
      "\n",
      "CNT_CHILDREN : 1770\n",
      "\n",
      "AMT_INCOME_TOTAL : 5642\n",
      "\n",
      "AMT_CREDIT : 2626\n",
      "\n",
      "AMT_ANNUITY : 3043\n",
      "\n",
      "AMT_GOODS_PRICE : 5814\n",
      "\n",
      "REGION_POPULATION_RELATIVE : 3403\n",
      "\n",
      "DAYS_BIRTH : 0\n",
      "\n",
      "DAYS_EMPLOYED : 28820\n",
      "\n",
      "DAYS_REGISTRATION : 287\n",
      "\n",
      "DAYS_ID_PUBLISH : 0\n",
      "\n",
      "FLAG_MOBIL : 0\n",
      "\n",
      "FLAG_EMP_PHONE : 22147\n",
      "\n",
      "FLAG_WORK_PHONE : 24570\n",
      "\n",
      "FLAG_CONT_MOBILE : 213\n",
      "\n",
      "FLAG_PHONE : 0\n",
      "\n",
      "FLAG_EMAIL : 6992\n",
      "\n",
      "CNT_FAM_MEMBERS : 1647\n",
      "\n",
      "REGION_RATING_CLIENT : 32366\n",
      "\n",
      "REGION_RATING_CLIENT_W_CITY : 31332\n",
      "\n",
      "HOUR_APPR_PROCESS_START : 911\n",
      "\n",
      "REG_REGION_NOT_LIVE_REGION : 1857\n",
      "\n",
      "REG_REGION_NOT_WORK_REGION : 6147\n",
      "\n",
      "LIVE_REGION_NOT_WORK_REGION : 4906\n",
      "\n",
      "REG_CITY_NOT_LIVE_CITY : 9504\n",
      "\n",
      "REG_CITY_NOT_WORK_CITY : 28371\n",
      "\n",
      "LIVE_CITY_NOT_WORK_CITY : 22141\n",
      "\n",
      "EXT_SOURCE_1 : 0\n",
      "\n",
      "EXT_SOURCE_2 : 0\n",
      "\n",
      "EXT_SOURCE_3 : 0\n",
      "\n",
      "APARTMENTS_AVG : 4343\n",
      "\n",
      "BASEMENTAREA_AVG : 2867\n",
      "\n",
      "YEARS_BEGINEXPLUATATION_AVG : 1887\n",
      "\n",
      "ELEVATORS_AVG : 4252\n",
      "\n",
      "ENTRANCES_AVG : 1556\n",
      "\n",
      "FLOORSMAX_AVG : 2066\n",
      "\n",
      "LANDAREA_AVG : 2748\n",
      "\n",
      "LIVINGAREA_AVG : 5074\n",
      "\n",
      "NONLIVINGAREA_AVG : 6547\n",
      "\n",
      "APARTMENTS_MODE : 4071\n",
      "\n",
      "BASEMENTAREA_MODE : 2748\n",
      "\n",
      "YEARS_BEGINEXPLUATATION_MODE : 1996\n",
      "\n",
      "ELEVATORS_MODE : 3989\n",
      "\n",
      "ENTRANCES_MODE : 1539\n",
      "\n",
      "FLOORSMAX_MODE : 2033\n",
      "\n",
      "LANDAREA_MODE : 2823\n",
      "\n",
      "LIVINGAREA_MODE : 5316\n",
      "\n",
      "NONLIVINGAREA_MODE : 7436\n",
      "\n",
      "APARTMENTS_MEDI : 4347\n",
      "\n",
      "BASEMENTAREA_MEDI : 2842\n",
      "\n",
      "YEARS_BEGINEXPLUATATION_MEDI : 1880\n",
      "\n",
      "ELEVATORS_MEDI : 4253\n",
      "\n",
      "ENTRANCES_MEDI : 1561\n",
      "\n",
      "FLOORSMAX_MEDI : 2128\n",
      "\n",
      "LANDAREA_MEDI : 2743\n",
      "\n",
      "LIVINGAREA_MEDI : 5227\n",
      "\n",
      "NONLIVINGAREA_MEDI : 6737\n",
      "\n",
      "TOTALAREA_MODE : 4864\n",
      "\n",
      "OBS_30_CNT_SOCIAL_CIRCLE : 7867\n",
      "\n",
      "DEF_30_CNT_SOCIAL_CIRCLE : 13992\n",
      "\n",
      "OBS_60_CNT_SOCIAL_CIRCLE : 7702\n",
      "\n",
      "DEF_60_CNT_SOCIAL_CIRCLE : 10250\n",
      "\n",
      "DAYS_LAST_PHONE_CHANGE : 190\n",
      "\n",
      "FLAG_DOCUMENT_2 : 6\n",
      "\n",
      "FLAG_DOCUMENT_3 : 0\n",
      "\n",
      "FLAG_DOCUMENT_4 : 8\n",
      "\n",
      "FLAG_DOCUMENT_5 : 1772\n",
      "\n",
      "FLAG_DOCUMENT_6 : 10779\n",
      "\n",
      "FLAG_DOCUMENT_7 : 26\n",
      "\n",
      "FLAG_DOCUMENT_8 : 9837\n",
      "\n",
      "FLAG_DOCUMENT_9 : 490\n",
      "\n",
      "FLAG_DOCUMENT_10 : 3\n",
      "\n",
      "FLAG_DOCUMENT_11 : 460\n",
      "\n",
      "FLAG_DOCUMENT_12 : 2\n",
      "\n",
      "FLAG_DOCUMENT_13 : 395\n",
      "\n",
      "FLAG_DOCUMENT_14 : 364\n",
      "\n",
      "FLAG_DOCUMENT_15 : 133\n",
      "\n",
      "FLAG_DOCUMENT_16 : 1268\n",
      "\n",
      "FLAG_DOCUMENT_17 : 32\n",
      "\n",
      "FLAG_DOCUMENT_18 : 1048\n",
      "\n",
      "FLAG_DOCUMENT_19 : 67\n",
      "\n",
      "FLAG_DOCUMENT_20 : 54\n",
      "\n",
      "FLAG_DOCUMENT_21 : 43\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_HOUR : 663\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_DAY : 568\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_WEEK : 3422\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_MON : 17650\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_QRT : 20068\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR : 1387\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_cols = test_data.dtypes != object\n",
    "test_cols = test_data.columns[test_cols].tolist()\n",
    "for i in range(len(train_cols)):\n",
    "    q1 = Q1_t[test_cols[i]]\n",
    "    q3 = Q3_t[test_cols[i]]\n",
    "    iqr = IQR_t[test_cols[i]]\n",
    "    outliers = test_data[((test_data[train_cols[i]] < (q1 - 1.5 * iqr)) | (test_data[train_cols[i]] > (q3 + 1.5 * iqr)))]\n",
    "    print(\"{} : {}\\n\".format(test_cols[i], outliers.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea07fe6",
   "metadata": {},
   "source": [
    "### Replacing Test Data outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6614b69",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_29048/1896934137.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_data[test_cols[i]] = np.where(test_data[test_cols[i]] > uw, uw, test_data[test_cols[i]])\n",
      "/tmp/ipykernel_29048/1896934137.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_data[test_cols[i]] = np.where(test_data[test_cols[i]] < lw, lw, test_data[test_cols[i]])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK_ID_CURR : 0\n",
      "\n",
      "CNT_CHILDREN : 0\n",
      "\n",
      "AMT_INCOME_TOTAL : 0\n",
      "\n",
      "AMT_CREDIT : 0\n",
      "\n",
      "AMT_ANNUITY : 0\n",
      "\n",
      "AMT_GOODS_PRICE : 0\n",
      "\n",
      "REGION_POPULATION_RELATIVE : 0\n",
      "\n",
      "DAYS_BIRTH : 0\n",
      "\n",
      "DAYS_EMPLOYED : 0\n",
      "\n",
      "DAYS_REGISTRATION : 0\n",
      "\n",
      "DAYS_ID_PUBLISH : 0\n",
      "\n",
      "FLAG_MOBIL : 0\n",
      "\n",
      "FLAG_EMP_PHONE : 0\n",
      "\n",
      "FLAG_WORK_PHONE : 0\n",
      "\n",
      "FLAG_CONT_MOBILE : 0\n",
      "\n",
      "FLAG_PHONE : 0\n",
      "\n",
      "FLAG_EMAIL : 0\n",
      "\n",
      "CNT_FAM_MEMBERS : 0\n",
      "\n",
      "REGION_RATING_CLIENT : 0\n",
      "\n",
      "REGION_RATING_CLIENT_W_CITY : 0\n",
      "\n",
      "HOUR_APPR_PROCESS_START : 0\n",
      "\n",
      "REG_REGION_NOT_LIVE_REGION : 0\n",
      "\n",
      "REG_REGION_NOT_WORK_REGION : 0\n",
      "\n",
      "LIVE_REGION_NOT_WORK_REGION : 0\n",
      "\n",
      "REG_CITY_NOT_LIVE_CITY : 0\n",
      "\n",
      "REG_CITY_NOT_WORK_CITY : 0\n",
      "\n",
      "LIVE_CITY_NOT_WORK_CITY : 0\n",
      "\n",
      "EXT_SOURCE_1 : 0\n",
      "\n",
      "EXT_SOURCE_2 : 0\n",
      "\n",
      "EXT_SOURCE_3 : 0\n",
      "\n",
      "APARTMENTS_AVG : 0\n",
      "\n",
      "BASEMENTAREA_AVG : 0\n",
      "\n",
      "YEARS_BEGINEXPLUATATION_AVG : 0\n",
      "\n",
      "ELEVATORS_AVG : 0\n",
      "\n",
      "ENTRANCES_AVG : 0\n",
      "\n",
      "FLOORSMAX_AVG : 0\n",
      "\n",
      "LANDAREA_AVG : 0\n",
      "\n",
      "LIVINGAREA_AVG : 0\n",
      "\n",
      "NONLIVINGAREA_AVG : 0\n",
      "\n",
      "APARTMENTS_MODE : 0\n",
      "\n",
      "BASEMENTAREA_MODE : 0\n",
      "\n",
      "YEARS_BEGINEXPLUATATION_MODE : 0\n",
      "\n",
      "ELEVATORS_MODE : 0\n",
      "\n",
      "ENTRANCES_MODE : 0\n",
      "\n",
      "FLOORSMAX_MODE : 0\n",
      "\n",
      "LANDAREA_MODE : 0\n",
      "\n",
      "LIVINGAREA_MODE : 0\n",
      "\n",
      "NONLIVINGAREA_MODE : 0\n",
      "\n",
      "APARTMENTS_MEDI : 0\n",
      "\n",
      "BASEMENTAREA_MEDI : 0\n",
      "\n",
      "YEARS_BEGINEXPLUATATION_MEDI : 0\n",
      "\n",
      "ELEVATORS_MEDI : 0\n",
      "\n",
      "ENTRANCES_MEDI : 0\n",
      "\n",
      "FLOORSMAX_MEDI : 0\n",
      "\n",
      "LANDAREA_MEDI : 0\n",
      "\n",
      "LIVINGAREA_MEDI : 0\n",
      "\n",
      "NONLIVINGAREA_MEDI : 0\n",
      "\n",
      "TOTALAREA_MODE : 0\n",
      "\n",
      "OBS_30_CNT_SOCIAL_CIRCLE : 0\n",
      "\n",
      "DEF_30_CNT_SOCIAL_CIRCLE : 0\n",
      "\n",
      "OBS_60_CNT_SOCIAL_CIRCLE : 0\n",
      "\n",
      "DEF_60_CNT_SOCIAL_CIRCLE : 0\n",
      "\n",
      "DAYS_LAST_PHONE_CHANGE : 0\n",
      "\n",
      "FLAG_DOCUMENT_2 : 0\n",
      "\n",
      "FLAG_DOCUMENT_3 : 0\n",
      "\n",
      "FLAG_DOCUMENT_4 : 0\n",
      "\n",
      "FLAG_DOCUMENT_5 : 0\n",
      "\n",
      "FLAG_DOCUMENT_6 : 0\n",
      "\n",
      "FLAG_DOCUMENT_7 : 0\n",
      "\n",
      "FLAG_DOCUMENT_8 : 0\n",
      "\n",
      "FLAG_DOCUMENT_9 : 0\n",
      "\n",
      "FLAG_DOCUMENT_10 : 0\n",
      "\n",
      "FLAG_DOCUMENT_11 : 0\n",
      "\n",
      "FLAG_DOCUMENT_12 : 0\n",
      "\n",
      "FLAG_DOCUMENT_13 : 0\n",
      "\n",
      "FLAG_DOCUMENT_14 : 0\n",
      "\n",
      "FLAG_DOCUMENT_15 : 0\n",
      "\n",
      "FLAG_DOCUMENT_16 : 0\n",
      "\n",
      "FLAG_DOCUMENT_17 : 0\n",
      "\n",
      "FLAG_DOCUMENT_18 : 0\n",
      "\n",
      "FLAG_DOCUMENT_19 : 0\n",
      "\n",
      "FLAG_DOCUMENT_20 : 0\n",
      "\n",
      "FLAG_DOCUMENT_21 : 0\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_HOUR : 0\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_DAY : 0\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_WEEK : 0\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_MON : 0\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_QRT : 0\n",
      "\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR : 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(train_cols)):\n",
    "    q1 = Q1_t[test_cols[i]]\n",
    "    q3 = Q3_t[test_cols[i]]\n",
    "    iqr = IQR_t[test_cols[i]]\n",
    "    uw = q3 + 1.5 * iqr\n",
    "    lw = q1 - 1.5 * iqr\n",
    "    test_data[test_cols[i]] = np.where(test_data[test_cols[i]] > uw, uw, test_data[test_cols[i]])\n",
    "    test_data[test_cols[i]] = np.where(test_data[test_cols[i]] < lw, lw, test_data[test_cols[i]])\n",
    "    outliers = test_data[((test_data[test_cols[i]] < (q1 - 1.5 * iqr)) | (test_data[test_cols[i]] > (q3 + 1.5 * iqr)))]\n",
    "    print(\"{} : {}\\n\".format(train_cols[i], outliers.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af7fe4af",
   "metadata": {},
   "source": [
    "## Filling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ead5200",
   "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",
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       "    }\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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LANDAREA_AVG</th>\n",
       "      <td>109543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_MODE</th>\n",
       "      <td>109543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_MEDI</th>\n",
       "      <td>109543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_MODE</th>\n",
       "      <td>107975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <td>107975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_HOUSING_TYPE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 0\n",
       "LANDAREA_AVG                109543\n",
       "LANDAREA_MODE               109543\n",
       "LANDAREA_MEDI               109543\n",
       "BASEMENTAREA_MODE           107975\n",
       "BASEMENTAREA_AVG            107975\n",
       "...                            ...\n",
       "REGION_POPULATION_RELATIVE       0\n",
       "NAME_HOUSING_TYPE                0\n",
       "NAME_FAMILY_STATUS               0\n",
       "NAME_EDUCATION_TYPE              0\n",
       "SK_ID_CURR                       0\n",
       "\n",
       "[104 rows x 1 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(train_data.isnull().sum().sort_values(ascending = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e5c8469",
   "metadata": {},
   "source": [
    "### missing values of numeric columns with mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a0d7999b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(train_cols)):\n",
    "    mean = train_data[train_cols[i]].mean()\n",
    "    train_data[train_cols[i]].fillna(value = mean, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "09c03ab5",
   "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",
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       "    }\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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LANDAREA_AVG</th>\n",
       "      <td>73047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_MODE</th>\n",
       "      <td>73047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_MEDI</th>\n",
       "      <td>73047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_MODE</th>\n",
       "      <td>71968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <td>71968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_HOUSING_TYPE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                0\n",
       "LANDAREA_AVG                73047\n",
       "LANDAREA_MODE               73047\n",
       "LANDAREA_MEDI               73047\n",
       "BASEMENTAREA_MODE           71968\n",
       "BASEMENTAREA_AVG            71968\n",
       "...                           ...\n",
       "REGION_POPULATION_RELATIVE      0\n",
       "NAME_HOUSING_TYPE               0\n",
       "NAME_FAMILY_STATUS              0\n",
       "NAME_EDUCATION_TYPE             0\n",
       "SK_ID_CURR                      0\n",
       "\n",
       "[104 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(test_data.isnull().sum().sort_values(ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "30b4bb0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_29048/207936572.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_data[train_cols[i]].fillna(value = mean, inplace = True)\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(test_cols)):\n",
    "    mean = test_data[test_cols[i]].mean()\n",
    "    test_data[train_cols[i]].fillna(value = mean, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6781acf",
   "metadata": {},
   "source": [
    "### missing values of categorical columns with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79bf4267",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.apply(lambda x:x.fillna(x.value_counts().index[0]))\n",
    "test_data = test_data.apply(lambda x:x.fillna(x.value_counts().index[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "272eeb6d",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGION_RATING_CLIENT_W_CITY</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGION_RATING_CLIENT</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0\n",
       "SK_ID_CURR                   0\n",
       "NAME_CONTRACT_TYPE           0\n",
       "DEF_60_CNT_SOCIAL_CIRCLE     0\n",
       "OBS_60_CNT_SOCIAL_CIRCLE     0\n",
       "DEF_30_CNT_SOCIAL_CIRCLE     0\n",
       "...                         ..\n",
       "HOUR_APPR_PROCESS_START      0\n",
       "WEEKDAY_APPR_PROCESS_START   0\n",
       "REGION_RATING_CLIENT_W_CITY  0\n",
       "REGION_RATING_CLIENT         0\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR   0\n",
       "\n",
       "[104 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(train_data.isnull().sum().sort_values(ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eb0cdb18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGION_RATING_CLIENT_W_CITY</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGION_RATING_CLIENT</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0\n",
       "SK_ID_CURR                   0\n",
       "NAME_CONTRACT_TYPE           0\n",
       "DEF_60_CNT_SOCIAL_CIRCLE     0\n",
       "OBS_60_CNT_SOCIAL_CIRCLE     0\n",
       "DEF_30_CNT_SOCIAL_CIRCLE     0\n",
       "...                         ..\n",
       "HOUR_APPR_PROCESS_START      0\n",
       "WEEKDAY_APPR_PROCESS_START   0\n",
       "REGION_RATING_CLIENT_W_CITY  0\n",
       "REGION_RATING_CLIENT         0\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR   0\n",
       "\n",
       "[104 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(test_data.isnull().sum().sort_values(ascending = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34160be9",
   "metadata": {},
   "source": [
    "## Label Encoding of categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f148f4f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_train_cols = train_data.dtypes == object\n",
    "cat_train_cols = train_data.columns[cat_train_cols].tolist()\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "for i in range(len(cat_train_cols)):\n",
    "    train_data[cat_train_cols[i]] = encoder.fit_transform(train_data[cat_train_cols[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d7164636",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_test_cols = test_data.dtypes == object\n",
    "cat_test_cols = test_data.columns[cat_test_cols].tolist()\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "for i in range(len(cat_test_cols)):\n",
    "    test_data[cat_test_cols[i]] = encoder.fit_transform(test_data[cat_test_cols[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91e0f121",
   "metadata": {},
   "outputs": [
    {
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       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
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       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>101880.0</td>\n",
       "      <td>10827.00</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>405000.0</td>\n",
       "      <td>20677.50</td>\n",
       "      <td>405000.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>414121.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>157500.0</td>\n",
       "      <td>888840.0</td>\n",
       "      <td>29506.50</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 104 columns</p>\n",
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       "   SK_ID_CURR  NAME_CONTRACT_TYPE  CODE_GENDER  FLAG_OWN_CAR  FLAG_OWN_REALTY  \\\n",
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       "1    271298.0                   0            1             1                1   \n",
       "2    122238.0                   0            1             1                1   \n",
       "3    305311.0                   0            1             0                0   \n",
       "4    414121.0                   0            0             0                1   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0           1.0           67500.0    227520.0     11065.50         180000.0   \n",
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       "\n",
       "   FLAG_DOCUMENT_21  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0               0.0                         0.0                        0.0   \n",
       "1               0.0                         0.0                        0.0   \n",
       "2               0.0                         0.0                        0.0   \n",
       "3               0.0                         0.0                        0.0   \n",
       "4               0.0                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         0.0                        0.0   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         2.0  \n",
       "1                        0.0                         3.0  \n",
       "2                        0.0                         1.0  \n",
       "3                        0.0                         2.0  \n",
       "4                        0.0                         2.0  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b8501680",
   "metadata": {},
   "outputs": [
    {
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       "      <td>346500.0</td>\n",
       "      <td>21069.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>337500.0</td>\n",
       "      <td>545040.0</td>\n",
       "      <td>36553.5</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>407238.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>135000.0</td>\n",
       "      <td>307557.0</td>\n",
       "      <td>20682.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>31018.5</td>\n",
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      ],
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       "   SK_ID_CURR  NAME_CONTRACT_TYPE  CODE_GENDER  FLAG_OWN_CAR  FLAG_OWN_REALTY  \\\n",
       "0    367294.0                   0            0             0                1   \n",
       "1    439847.0                   0            0             0                1   \n",
       "2    380562.0                   0            1             1                0   \n",
       "3    407238.0                   0            0             0                1   \n",
       "4    239910.0                   0            0             0                1   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0           0.0          180000.0    265306.5      25317.0         252000.0   \n",
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       "\n",
       "   ...  FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  \\\n",
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       "\n",
       "   FLAG_DOCUMENT_21  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0               0.0                         0.0                        0.0   \n",
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       "2               0.0                         0.0                        0.0   \n",
       "3               0.0                         0.0                        0.0   \n",
       "4               0.0                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         0.0                        0.0   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                    5.000000  \n",
       "1                        0.0                    5.000000  \n",
       "2                        0.0                    5.000000  \n",
       "3                        0.0                    2.000000  \n",
       "4                        0.0                    1.889528  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8706a5c",
   "metadata": {},
   "source": [
    "Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aed55bd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ID = test_data['SK_ID_CURR']\n",
    "col = train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dd4771e1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
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       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
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       "      <td>180000.0</td>\n",
       "      <td>101880.0</td>\n",
       "      <td>10827.00</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>888840.0</td>\n",
       "      <td>29506.50</td>\n",
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       "<p>5 rows × 104 columns</p>\n",
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       "   SK_ID_CURR  NAME_CONTRACT_TYPE  CODE_GENDER  FLAG_OWN_CAR  FLAG_OWN_REALTY  \\\n",
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       "2    122238.0                   0            1             1                1   \n",
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       "4    414121.0                   0            0             0                1   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0           1.0           67500.0    227520.0     11065.50         180000.0   \n",
       "1           1.0          247500.0   1616625.0     61656.75        1341000.0   \n",
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       "3           0.0           81000.0    405000.0     20677.50         405000.0   \n",
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       "\n",
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       "\n",
       "   FLAG_DOCUMENT_21  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0               0.0                         0.0                        0.0   \n",
       "1               0.0                         0.0                        0.0   \n",
       "2               0.0                         0.0                        0.0   \n",
       "3               0.0                         0.0                        0.0   \n",
       "4               0.0                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         0.0                        0.0   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         2.0  \n",
       "1                        0.0                         3.0  \n",
       "2                        0.0                         1.0  \n",
       "3                        0.0                         2.0  \n",
       "4                        0.0                         2.0  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b73f61b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>265306.5</td>\n",
       "      <td>25317.0</td>\n",
       "      <td>252000.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>439847.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>346500.0</td>\n",
       "      <td>21069.0</td>\n",
       "      <td>346500.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>380562.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>337500.0</td>\n",
       "      <td>545040.0</td>\n",
       "      <td>36553.5</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>407238.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>307557.0</td>\n",
       "      <td>20682.0</td>\n",
       "      <td>265500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>239910.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>1056447.0</td>\n",
       "      <td>31018.5</td>\n",
       "      <td>922500.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "<p>5 rows × 104 columns</p>\n",
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      "text/plain": [
       "   SK_ID_CURR  NAME_CONTRACT_TYPE  CODE_GENDER  FLAG_OWN_CAR  FLAG_OWN_REALTY  \\\n",
       "0    367294.0                   0            0             0                1   \n",
       "1    439847.0                   0            0             0                1   \n",
       "2    380562.0                   0            1             1                0   \n",
       "3    407238.0                   0            0             0                1   \n",
       "4    239910.0                   0            0             0                1   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0           0.0          180000.0    265306.5      25317.0         252000.0   \n",
       "1           0.0          202500.0    346500.0      21069.0         346500.0   \n",
       "2           0.0          337500.0    545040.0      36553.5         450000.0   \n",
       "3           0.0          135000.0    307557.0      20682.0         265500.0   \n",
       "4           0.0          157500.0   1056447.0      31018.5         922500.0   \n",
       "\n",
       "   ...  FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  \\\n",
       "0  ...               0.0               0.0               0.0   \n",
       "1  ...               0.0               0.0               0.0   \n",
       "2  ...               0.0               0.0               0.0   \n",
       "3  ...               0.0               0.0               0.0   \n",
       "4  ...               0.0               0.0               0.0   \n",
       "\n",
       "   FLAG_DOCUMENT_21  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0               0.0                         0.0                        0.0   \n",
       "1               0.0                         0.0                        0.0   \n",
       "2               0.0                         0.0                        0.0   \n",
       "3               0.0                         0.0                        0.0   \n",
       "4               0.0                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         0.0                        0.0   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                    5.000000  \n",
       "1                        0.0                    5.000000  \n",
       "2                        0.0                    5.000000  \n",
       "3                        0.0                    2.000000  \n",
       "4                        0.0                    1.889528  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e17bc4e0",
   "metadata": {},
   "source": [
    "## Applying models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d669623",
   "metadata": {},
   "source": [
    "### LGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2a764cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb = LGBMClassifier(**{'reg_lambda': 0.1, \n",
    "                        'reg_alpha': 0.2, \n",
    "                        'num_leaves': 125,  \n",
    "                        'n_estimators': 300, \n",
    "                        'min_child_samples': 800, \n",
    "                        'learning_rate': 0.1505,\n",
    "                        'max_bin': 500,\n",
    "                        'objective': 'binary',\n",
    "                        'n_jobs': -1,\n",
    "                        'class_weight':'balanced',\n",
    "                        'random_state':100})\n",
    "\n",
    "lgb.fit(train_data, y_train)\n",
    "y_pred_train = lgb.predict(train_data)\n",
    "y_pred_test = lgb.predict(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57d80f7",
   "metadata": {},
   "source": [
    "## Making output file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0d2b98b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         367294\n",
       "1         439847\n",
       "2         380562\n",
       "3         407238\n",
       "4         239910\n",
       "           ...  \n",
       "123000    128638\n",
       "123001    169821\n",
       "123002    442166\n",
       "123003    301605\n",
       "123004    176833\n",
       "Name: SK_ID_CURR, Length: 123005, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID = ID.astype(int)\n",
    "ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "756d11a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame({'SK_ID_CURR' : ID, 'TARGET' : y_pred_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7398340b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET\n",
       "0      367294       0\n",
       "1      439847       0\n",
       "2      380562       0\n",
       "3      407238       0\n",
       "4      239910       0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "19cd0558",
   "metadata": {},
   "outputs": [],
   "source": [
    "output.to_csv('submission1.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfca810f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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