task-3-project/SoberMLEngineers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Project\n",
    "Sober ML Engineers\n",
    "1. Shashank Shekar \n",
    "2. Chinthan Chandra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries and Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import f1_score,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_description=pd.read_csv(\"columns_description.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Row</th>\n",
       "      <th>Description</th>\n",
       "      <th>Special</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SK_ID_CURR</td>\n",
       "      <td>ID of loan in our sample</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TARGET</td>\n",
       "      <td>Target variable (1 - client with payment diffi...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NAME_CONTRACT_TYPE</td>\n",
       "      <td>Identification if loan is cash or revolving</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CODE_GENDER</td>\n",
       "      <td>Gender of the client</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FLAG_OWN_CAR</td>\n",
       "      <td>Flag if the client owns a car</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_DAY</td>\n",
       "      <td>Number of enquiries to Credit Bureau about the...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_WEEK</td>\n",
       "      <td>Number of enquiries to Credit Bureau about the...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_MON</td>\n",
       "      <td>Number of enquiries to Credit Bureau about the...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_QRT</td>\n",
       "      <td>Number of enquiries to Credit Bureau about the...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_YEAR</td>\n",
       "      <td>Number of enquiries to Credit Bureau about the...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Row  \\\n",
       "0                    SK_ID_CURR   \n",
       "1                        TARGET   \n",
       "2            NAME_CONTRACT_TYPE   \n",
       "3                   CODE_GENDER   \n",
       "4                  FLAG_OWN_CAR   \n",
       "..                          ...   \n",
       "117   AMT_REQ_CREDIT_BUREAU_DAY   \n",
       "118  AMT_REQ_CREDIT_BUREAU_WEEK   \n",
       "119   AMT_REQ_CREDIT_BUREAU_MON   \n",
       "120   AMT_REQ_CREDIT_BUREAU_QRT   \n",
       "121  AMT_REQ_CREDIT_BUREAU_YEAR   \n",
       "\n",
       "                                           Description Special  \n",
       "0                             ID of loan in our sample     NaN  \n",
       "1    Target variable (1 - client with payment diffi...     NaN  \n",
       "2          Identification if loan is cash or revolving     NaN  \n",
       "3                                 Gender of the client     NaN  \n",
       "4                        Flag if the client owns a car     NaN  \n",
       "..                                                 ...     ...  \n",
       "117  Number of enquiries to Credit Bureau about the...     NaN  \n",
       "118  Number of enquiries to Credit Bureau about the...     NaN  \n",
       "119  Number of enquiries to Credit Bureau about the...     NaN  \n",
       "120  Number of enquiries to Credit Bureau about the...     NaN  \n",
       "121  Number of enquiries to Credit Bureau about the...     NaN  \n",
       "\n",
       "[122 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=pd.read_csv(\"train_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TARGET', ylabel='count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_data[\"TARGET\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9192709180189262"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((train_data[\"TARGET\"]==0).sum())/train_data[\"TARGET\"].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=pd.read_csv(\"test_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission=pd.read_csv(\"./sample_solutions.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(184506, 122)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(123005, 121)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(184506, 122)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(123005, 121)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing Null Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for the percentage of null values in each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK_ID_CURR                     0.000000\n",
      "NAME_CONTRACT_TYPE             0.000000\n",
      "CODE_GENDER                    0.000000\n",
      "FLAG_OWN_CAR                   0.000000\n",
      "FLAG_OWN_REALTY                0.000000\n",
      "                                ...    \n",
      "AMT_REQ_CREDIT_BUREAU_WEEK    13.548611\n",
      "AMT_REQ_CREDIT_BUREAU_MON     13.548611\n",
      "AMT_REQ_CREDIT_BUREAU_QRT     13.548611\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR    13.548611\n",
      "TARGET                         0.000000\n",
      "Length: 122, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Nan_percentage_in_train_data=(train_data.isna().sum()/train_data.shape[0])*100\n",
    "print(Nan_percentage_in_train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK_ID_CURR                     0.000000\n",
      "NAME_CONTRACT_TYPE             0.000000\n",
      "CODE_GENDER                    0.000000\n",
      "FLAG_OWN_CAR                   0.000000\n",
      "FLAG_OWN_REALTY                0.000000\n",
      "                                ...    \n",
      "AMT_REQ_CREDIT_BUREAU_DAY     13.431161\n",
      "AMT_REQ_CREDIT_BUREAU_WEEK    13.431161\n",
      "AMT_REQ_CREDIT_BUREAU_MON     13.431161\n",
      "AMT_REQ_CREDIT_BUREAU_QRT     13.431161\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR    13.431161\n",
      "Length: 121, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Nan_percentage_in_test_data=(test_data.isna().sum()/test_data.shape[0])*100\n",
    "print(Nan_percentage_in_test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping the columns which have more than 40% of the data as Nan values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in train_data:\n",
    "\tif(Nan_percentage_in_train_data[column]>40):\n",
    "\t\ttrain_data.drop(axis=\"columns\",labels=column,inplace=True)\n",
    "\t\ttest_data.drop(axis=\"columns\",labels=column,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "nunu=train_data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(184506, 73)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(123005, 72)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns with minute null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping the rows for the columns with very minute null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(axis=\"rows\",labels=train_data.index[train_data[\"AMT_ANNUITY\"].isna()],inplace=True) \n",
    "train_data.drop(axis=\"rows\",labels=train_data.index[train_data[\"CNT_FAM_MEMBERS\"].isna()],inplace=True)\n",
    "train_data.drop(axis=\"rows\",labels=train_data.index[train_data[\"DAYS_LAST_PHONE_CHANGE\"].isna()],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the test data since we can't drop the rows, we impute some value in these minute null value columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"CNT_FAM_MEMBERS\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0     63184\n",
       "1.0     27354\n",
       "3.0     20781\n",
       "4.0     10038\n",
       "5.0      1411\n",
       "6.0       187\n",
       "7.0        36\n",
       "8.0         8\n",
       "9.0         1\n",
       "12.0        1\n",
       "13.0        1\n",
       "20.0        1\n",
       "15.0        1\n",
       "Name: CNT_FAM_MEMBERS, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"CNT_FAM_MEMBERS\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputing 2 as it is the majority."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"CNT_FAM_MEMBERS\"].fillna(2,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"AMT_ANNUITY\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    122999.000000\n",
       "mean      27099.750994\n",
       "std       14531.338692\n",
       "min        1980.000000\n",
       "25%       16501.500000\n",
       "50%       24853.500000\n",
       "75%       34596.000000\n",
       "max      230161.500000\n",
       "Name: AMT_ANNUITY, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"AMT_ANNUITY\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"AMT_ANNUITY\"].fillna(test_data[\"AMT_ANNUITY\"].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We impute some value to the null values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AMT_REQ_CREDIT_BUREAU_DAY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    158582\n",
       "NaN     24995\n",
       "1.0       790\n",
       "2.0        69\n",
       "3.0        30\n",
       "4.0        20\n",
       "6.0         5\n",
       "5.0         5\n",
       "8.0         1\n",
       "9.0         1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_DAY, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"AMT_REQ_CREDIT_BUREAU_DAY\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"AMT_REQ_CREDIT_BUREAU_DAY\"].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    183577\n",
       "1.0       790\n",
       "2.0        69\n",
       "3.0        30\n",
       "4.0        20\n",
       "6.0         5\n",
       "5.0         5\n",
       "8.0         1\n",
       "9.0         1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_DAY, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"AMT_REQ_CREDIT_BUREAU_DAY\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    105916\n",
       "NaN     16521\n",
       "1.0       502\n",
       "2.0        37\n",
       "3.0        15\n",
       "4.0         6\n",
       "5.0         4\n",
       "6.0         3\n",
       "9.0         1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_DAY, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"AMT_REQ_CREDIT_BUREAU_DAY\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"AMT_REQ_CREDIT_BUREAU_DAY\"].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    122437\n",
       "1.0       502\n",
       "2.0        37\n",
       "3.0        15\n",
       "4.0         6\n",
       "5.0         4\n",
       "6.0         3\n",
       "9.0         1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_DAY, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"AMT_REQ_CREDIT_BUREAU_DAY\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AMT_REQ_CREDIT_BUREAU_HOUR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    158540\n",
       "NaN     24995\n",
       "1.0       923\n",
       "2.0        33\n",
       "3.0         6\n",
       "4.0         1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_HOUR, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_HOUR'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_HOUR'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    183535\n",
       "1.0       923\n",
       "2.0        33\n",
       "3.0         6\n",
       "4.0         1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_HOUR, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_HOUR'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    105821\n",
       "NaN     16521\n",
       "1.0       637\n",
       "2.0        23\n",
       "3.0         3\n",
       "Name: AMT_REQ_CREDIT_BUREAU_HOUR, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_HOUR'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_HOUR'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    122342\n",
       "1.0       637\n",
       "2.0        23\n",
       "3.0         3\n",
       "Name: AMT_REQ_CREDIT_BUREAU_HOUR, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_HOUR'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AMT_REQ_CREDIT_BUREAU_WEEK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    154390\n",
       "NaN     24995\n",
       "1.0      4902\n",
       "2.0       131\n",
       "3.0        38\n",
       "4.0        19\n",
       "6.0        13\n",
       "5.0         8\n",
       "7.0         2\n",
       "Name: AMT_REQ_CREDIT_BUREAU_WEEK, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_WEEK'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_WEEK'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    179385\n",
       "1.0      4902\n",
       "2.0       131\n",
       "3.0        38\n",
       "4.0        19\n",
       "6.0        13\n",
       "5.0         8\n",
       "7.0         2\n",
       "Name: AMT_REQ_CREDIT_BUREAU_WEEK, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_WEEK'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    103062\n",
       "NaN     16521\n",
       "1.0      3305\n",
       "2.0        68\n",
       "3.0        20\n",
       "4.0        15\n",
       "6.0         7\n",
       "8.0         5\n",
       "5.0         2\n",
       "Name: AMT_REQ_CREDIT_BUREAU_WEEK, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_WEEK'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_WEEK'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    119583\n",
       "1.0      3305\n",
       "2.0        68\n",
       "3.0        20\n",
       "4.0        15\n",
       "6.0         7\n",
       "8.0         5\n",
       "5.0         2\n",
       "Name: AMT_REQ_CREDIT_BUREAU_WEEK, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_WEEK'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AMT_REQ_CREDIT_BUREAU_MON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     133397\n",
       "NaN      24995\n",
       "1.0      19751\n",
       "2.0       3188\n",
       "3.0       1207\n",
       "4.0        647\n",
       "5.0        371\n",
       "6.0        216\n",
       "7.0        166\n",
       "9.0        132\n",
       "8.0        110\n",
       "10.0        75\n",
       "11.0        74\n",
       "13.0        48\n",
       "12.0        44\n",
       "14.0        27\n",
       "15.0        21\n",
       "16.0        14\n",
       "17.0         7\n",
       "18.0         4\n",
       "19.0         2\n",
       "22.0         1\n",
       "24.0         1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_MON, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_MON'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_MON'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     158392\n",
       "1.0      19751\n",
       "2.0       3188\n",
       "3.0       1207\n",
       "4.0        647\n",
       "5.0        371\n",
       "6.0        216\n",
       "7.0        166\n",
       "9.0        132\n",
       "8.0        110\n",
       "10.0        75\n",
       "11.0        74\n",
       "13.0        48\n",
       "12.0        44\n",
       "14.0        27\n",
       "15.0        21\n",
       "16.0        14\n",
       "17.0         7\n",
       "18.0         4\n",
       "19.0         2\n",
       "22.0         1\n",
       "24.0         1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_MON, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_MON'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     88834\n",
       "NaN     16521\n",
       "1.0     13393\n",
       "2.0      2198\n",
       "3.0       784\n",
       "4.0       429\n",
       "5.0       231\n",
       "7.0       132\n",
       "6.0       127\n",
       "8.0        75\n",
       "9.0        74\n",
       "10.0       57\n",
       "11.0       45\n",
       "12.0       33\n",
       "13.0       24\n",
       "15.0       14\n",
       "14.0       13\n",
       "16.0        9\n",
       "17.0        7\n",
       "18.0        2\n",
       "27.0        1\n",
       "23.0        1\n",
       "19.0        1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_MON, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_MON'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_MON'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     105355\n",
       "1.0      13393\n",
       "2.0       2198\n",
       "3.0        784\n",
       "4.0        429\n",
       "5.0        231\n",
       "7.0        132\n",
       "6.0        127\n",
       "8.0         75\n",
       "9.0         74\n",
       "10.0        57\n",
       "11.0        45\n",
       "12.0        33\n",
       "13.0        24\n",
       "15.0        14\n",
       "14.0        13\n",
       "16.0         9\n",
       "17.0         7\n",
       "18.0         2\n",
       "27.0         1\n",
       "23.0         1\n",
       "19.0         1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_MON, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_MON'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AMT_REQ_CREDIT_BUREAU_QRT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0      128997\n",
       "NaN       24995\n",
       "1.0       20522\n",
       "2.0        8626\n",
       "3.0        1022\n",
       "4.0         271\n",
       "5.0          40\n",
       "6.0          18\n",
       "8.0           4\n",
       "7.0           1\n",
       "261.0         1\n",
       "19.0          1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_QRT, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_QRT'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_QRT'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0      153992\n",
       "1.0       20522\n",
       "2.0        8626\n",
       "3.0        1022\n",
       "4.0         271\n",
       "5.0          40\n",
       "6.0          18\n",
       "8.0           4\n",
       "7.0           1\n",
       "261.0         1\n",
       "19.0          1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_QRT, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_QRT'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    86416\n",
       "NaN    16521\n",
       "1.0    13339\n",
       "2.0     5786\n",
       "3.0      695\n",
       "4.0      205\n",
       "5.0       24\n",
       "6.0       10\n",
       "7.0        6\n",
       "8.0        3\n",
       "Name: AMT_REQ_CREDIT_BUREAU_QRT, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_QRT'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_QRT'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    102937\n",
       "1.0     13339\n",
       "2.0      5786\n",
       "3.0       695\n",
       "4.0       205\n",
       "5.0        24\n",
       "6.0        10\n",
       "7.0         6\n",
       "8.0         3\n",
       "Name: AMT_REQ_CREDIT_BUREAU_QRT, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_QRT'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AMT_REQ_CREDIT_BUREAU_YEAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     43204\n",
       "1.0     37825\n",
       "2.0     30085\n",
       "NaN     24995\n",
       "3.0     20299\n",
       "4.0     12432\n",
       "5.0      7196\n",
       "6.0      4142\n",
       "7.0      2344\n",
       "8.0      1263\n",
       "9.0       645\n",
       "11.0       18\n",
       "12.0       16\n",
       "13.0       10\n",
       "10.0        9\n",
       "17.0        3\n",
       "19.0        3\n",
       "15.0        3\n",
       "14.0        2\n",
       "25.0        1\n",
       "16.0        1\n",
       "23.0        1\n",
       "20.0        1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_YEAR, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_YEAR'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AMT_REQ_CREDIT_BUREAU_YEAR', ylabel='Count'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train_data[\"AMT_REQ_CREDIT_BUREAU_YEAR\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly making the null values causes the value 0 to become marginally higher than 1. So we impute this with one of the central tendencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    159503.000000\n",
       "mean          1.897419\n",
       "std           1.864304\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           1.000000\n",
       "75%           3.000000\n",
       "max          25.000000\n",
       "Name: AMT_REQ_CREDIT_BUREAU_YEAR, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"AMT_REQ_CREDIT_BUREAU_YEAR\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mode and median are both 0, so we will use the mean to replace the null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_YEAR'].fillna(train_data['AMT_REQ_CREDIT_BUREAU_YEAR'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000000     43204\n",
       "1.000000     37825\n",
       "2.000000     30085\n",
       "1.897419     24995\n",
       "3.000000     20299\n",
       "4.000000     12432\n",
       "5.000000      7196\n",
       "6.000000      4142\n",
       "7.000000      2344\n",
       "8.000000      1263\n",
       "9.000000       645\n",
       "11.000000       18\n",
       "12.000000       16\n",
       "13.000000       10\n",
       "10.000000        9\n",
       "17.000000        3\n",
       "19.000000        3\n",
       "15.000000        3\n",
       "14.000000        2\n",
       "25.000000        1\n",
       "16.000000        1\n",
       "23.000000        1\n",
       "20.000000        1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_YEAR, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['AMT_REQ_CREDIT_BUREAU_YEAR'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do the same thing for test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     28597\n",
       "1.0     25579\n",
       "2.0     20106\n",
       "NaN     16521\n",
       "3.0     13329\n",
       "4.0      8281\n",
       "5.0      4855\n",
       "6.0      2825\n",
       "7.0      1525\n",
       "8.0       864\n",
       "9.0       451\n",
       "12.0       14\n",
       "10.0       13\n",
       "11.0       12\n",
       "13.0        9\n",
       "14.0        8\n",
       "18.0        4\n",
       "17.0        4\n",
       "15.0        3\n",
       "16.0        2\n",
       "21.0        1\n",
       "22.0        1\n",
       "19.0        1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_YEAR, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_YEAR'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_YEAR'].fillna(test_data['AMT_REQ_CREDIT_BUREAU_YEAR'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000000     28597\n",
       "1.000000     25579\n",
       "2.000000     20106\n",
       "1.903676     16521\n",
       "3.000000     13329\n",
       "4.000000      8281\n",
       "5.000000      4855\n",
       "6.000000      2825\n",
       "7.000000      1525\n",
       "8.000000       864\n",
       "9.000000       451\n",
       "12.000000       14\n",
       "10.000000       13\n",
       "11.000000       12\n",
       "13.000000        9\n",
       "14.000000        8\n",
       "18.000000        4\n",
       "17.000000        4\n",
       "15.000000        3\n",
       "16.000000        2\n",
       "21.000000        1\n",
       "22.000000        1\n",
       "19.000000        1\n",
       "Name: AMT_REQ_CREDIT_BUREAU_YEAR, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['AMT_REQ_CREDIT_BUREAU_YEAR'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OCCUPATION_TYPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN                      57866\n",
       "Laborers                 32967\n",
       "Sales staff              19351\n",
       "Core staff               16491\n",
       "Managers                 12779\n",
       "Drivers                  11237\n",
       "High skill tech staff     6859\n",
       "Accountants               5896\n",
       "Medicine staff            5130\n",
       "Security staff            4073\n",
       "Cooking staff             3621\n",
       "Cleaning staff            2766\n",
       "Private service staff     1565\n",
       "Low-skill Laborers        1223\n",
       "Waiters/barmen staff       819\n",
       "Secretaries                786\n",
       "Realty agents              434\n",
       "HR staff                   321\n",
       "IT staff                   314\n",
       "Name: OCCUPATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"OCCUPATION_TYPE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='OCCUPATION_TYPE', ylabel='Count'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,16))\n",
    "sns.histplot(train_data[\"OCCUPATION_TYPE\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is categorical we will just consider the null values as another category. If we have to change the null values to another category this will cause the column to be biased towards one category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"OCCUPATION_TYPE\"].fillna(\"Unemployed\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unemployed               57866\n",
       "Laborers                 32967\n",
       "Sales staff              19351\n",
       "Core staff               16491\n",
       "Managers                 12779\n",
       "Drivers                  11237\n",
       "High skill tech staff     6859\n",
       "Accountants               5896\n",
       "Medicine staff            5130\n",
       "Security staff            4073\n",
       "Cooking staff             3621\n",
       "Cleaning staff            2766\n",
       "Private service staff     1565\n",
       "Low-skill Laborers        1223\n",
       "Waiters/barmen staff       819\n",
       "Secretaries                786\n",
       "Realty agents              434\n",
       "HR staff                   321\n",
       "IT staff                   314\n",
       "Name: OCCUPATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"OCCUPATION_TYPE\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN                      38524\n",
       "Laborers                 22218\n",
       "Sales staff              12751\n",
       "Core staff               11078\n",
       "Managers                  8591\n",
       "Drivers                   7365\n",
       "High skill tech staff     4521\n",
       "Accountants               3916\n",
       "Medicine staff            3407\n",
       "Security staff            2647\n",
       "Cooking staff             2325\n",
       "Cleaning staff            1887\n",
       "Private service staff     1087\n",
       "Low-skill Laborers         870\n",
       "Waiters/barmen staff       529\n",
       "Secretaries                518\n",
       "Realty agents              317\n",
       "HR staff                   242\n",
       "IT staff                   212\n",
       "Name: OCCUPATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"OCCUPATION_TYPE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"OCCUPATION_TYPE\"].fillna(\"Unemployed\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unemployed               38524\n",
       "Laborers                 22218\n",
       "Sales staff              12751\n",
       "Core staff               11078\n",
       "Managers                  8591\n",
       "Drivers                   7365\n",
       "High skill tech staff     4521\n",
       "Accountants               3916\n",
       "Medicine staff            3407\n",
       "Security staff            2647\n",
       "Cooking staff             2325\n",
       "Cleaning staff            1887\n",
       "Private service staff     1087\n",
       "Low-skill Laborers         870\n",
       "Waiters/barmen staff       529\n",
       "Secretaries                518\n",
       "Realty agents              317\n",
       "HR staff                   242\n",
       "IT staff                   212\n",
       "Name: OCCUPATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"OCCUPATION_TYPE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OBS_30CNT_SOCIAL_CIRCLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0      98233\n",
       "1.0      29319\n",
       "2.0      17861\n",
       "3.0      12096\n",
       "4.0       8516\n",
       "5.0       5730\n",
       "6.0       3971\n",
       "7.0       2660\n",
       "8.0       1782\n",
       "9.0       1205\n",
       "10.0       814\n",
       "NaN        639\n",
       "11.0       507\n",
       "12.0       412\n",
       "13.0       234\n",
       "14.0       161\n",
       "15.0       104\n",
       "16.0        69\n",
       "17.0        48\n",
       "18.0        29\n",
       "19.0        27\n",
       "20.0        18\n",
       "21.0        15\n",
       "22.0        14\n",
       "23.0        11\n",
       "25.0         8\n",
       "24.0         7\n",
       "27.0         3\n",
       "30.0         2\n",
       "29.0         1\n",
       "348.0        1\n",
       "26.0         1\n",
       "Name: OBS_30_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"OBS_30_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"OBS_30_CNT_SOCIAL_CIRCLE\"].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0      98872\n",
       "1.0      29319\n",
       "2.0      17861\n",
       "3.0      12096\n",
       "4.0       8516\n",
       "5.0       5730\n",
       "6.0       3971\n",
       "7.0       2660\n",
       "8.0       1782\n",
       "9.0       1205\n",
       "10.0       814\n",
       "11.0       507\n",
       "12.0       412\n",
       "13.0       234\n",
       "14.0       161\n",
       "15.0       104\n",
       "16.0        69\n",
       "17.0        48\n",
       "18.0        29\n",
       "19.0        27\n",
       "20.0        18\n",
       "21.0        15\n",
       "22.0        14\n",
       "23.0        11\n",
       "25.0         8\n",
       "24.0         7\n",
       "27.0         3\n",
       "30.0         2\n",
       "29.0         1\n",
       "348.0        1\n",
       "26.0         1\n",
       "Name: OBS_30_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"OBS_30_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     65670\n",
       "1.0     19464\n",
       "2.0     11947\n",
       "3.0      8225\n",
       "4.0      5627\n",
       "5.0      3823\n",
       "6.0      2482\n",
       "7.0      1730\n",
       "8.0      1185\n",
       "9.0       798\n",
       "10.0      562\n",
       "NaN       382\n",
       "11.0      345\n",
       "12.0      240\n",
       "13.0      177\n",
       "14.0       97\n",
       "16.0       64\n",
       "15.0       62\n",
       "17.0       40\n",
       "19.0       17\n",
       "18.0       17\n",
       "21.0       14\n",
       "20.0       12\n",
       "22.0        8\n",
       "23.0        4\n",
       "24.0        4\n",
       "25.0        3\n",
       "26.0        2\n",
       "27.0        2\n",
       "47.0        1\n",
       "28.0        1\n",
       "Name: OBS_30_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"OBS_30_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"OBS_30_CNT_SOCIAL_CIRCLE\"].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0      98872\n",
       "1.0      29319\n",
       "2.0      17861\n",
       "3.0      12096\n",
       "4.0       8516\n",
       "5.0       5730\n",
       "6.0       3971\n",
       "7.0       2660\n",
       "8.0       1782\n",
       "9.0       1205\n",
       "10.0       814\n",
       "11.0       507\n",
       "12.0       412\n",
       "13.0       234\n",
       "14.0       161\n",
       "15.0       104\n",
       "16.0        69\n",
       "17.0        48\n",
       "18.0        29\n",
       "19.0        27\n",
       "20.0        18\n",
       "21.0        15\n",
       "22.0        14\n",
       "23.0        11\n",
       "25.0         8\n",
       "24.0         7\n",
       "27.0         3\n",
       "30.0         2\n",
       "29.0         1\n",
       "348.0        1\n",
       "26.0         1\n",
       "Name: OBS_30_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"OBS_30_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OBS_60_CNT_SOCIAL_CIRCLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0      98693\n",
       "1.0      29358\n",
       "2.0      17846\n",
       "3.0      12037\n",
       "4.0       8387\n",
       "5.0       5676\n",
       "6.0       3898\n",
       "7.0       2642\n",
       "8.0       1733\n",
       "9.0       1182\n",
       "10.0       788\n",
       "NaN        639\n",
       "11.0       497\n",
       "12.0       392\n",
       "13.0       231\n",
       "14.0       149\n",
       "15.0       104\n",
       "16.0        70\n",
       "17.0        39\n",
       "18.0        35\n",
       "19.0        24\n",
       "20.0        20\n",
       "22.0        13\n",
       "21.0        13\n",
       "23.0        10\n",
       "24.0         8\n",
       "25.0         7\n",
       "27.0         3\n",
       "29.0         2\n",
       "344.0        1\n",
       "30.0         1\n",
       "Name: OBS_60_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"OBS_60_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"OBS_60_CNT_SOCIAL_CIRCLE\"].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0      99332\n",
       "1.0      29358\n",
       "2.0      17846\n",
       "3.0      12037\n",
       "4.0       8387\n",
       "5.0       5676\n",
       "6.0       3898\n",
       "7.0       2642\n",
       "8.0       1733\n",
       "9.0       1182\n",
       "10.0       788\n",
       "11.0       497\n",
       "12.0       392\n",
       "13.0       231\n",
       "14.0       149\n",
       "15.0       104\n",
       "16.0        70\n",
       "17.0        39\n",
       "18.0        35\n",
       "19.0        24\n",
       "20.0        20\n",
       "22.0        13\n",
       "21.0        13\n",
       "23.0        10\n",
       "24.0         8\n",
       "25.0         7\n",
       "27.0         3\n",
       "29.0         2\n",
       "344.0        1\n",
       "30.0         1\n",
       "Name: OBS_60_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"OBS_60_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     65966\n",
       "1.0     19512\n",
       "2.0     11920\n",
       "3.0      8177\n",
       "4.0      5559\n",
       "5.0      3787\n",
       "6.0      2451\n",
       "7.0      1702\n",
       "8.0      1153\n",
       "9.0       777\n",
       "10.0      547\n",
       "NaN       382\n",
       "11.0      340\n",
       "12.0      228\n",
       "13.0      170\n",
       "14.0       89\n",
       "16.0       67\n",
       "15.0       62\n",
       "17.0       34\n",
       "19.0       20\n",
       "21.0       15\n",
       "18.0       14\n",
       "20.0       10\n",
       "22.0        7\n",
       "25.0        4\n",
       "24.0        4\n",
       "23.0        3\n",
       "27.0        2\n",
       "47.0        1\n",
       "28.0        1\n",
       "26.0        1\n",
       "Name: OBS_60_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"OBS_60_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"OBS_60_CNT_SOCIAL_CIRCLE\"].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0      99332\n",
       "1.0      29358\n",
       "2.0      17846\n",
       "3.0      12037\n",
       "4.0       8387\n",
       "5.0       5676\n",
       "6.0       3898\n",
       "7.0       2642\n",
       "8.0       1733\n",
       "9.0       1182\n",
       "10.0       788\n",
       "11.0       497\n",
       "12.0       392\n",
       "13.0       231\n",
       "14.0       149\n",
       "15.0       104\n",
       "16.0        70\n",
       "17.0        39\n",
       "18.0        35\n",
       "19.0        24\n",
       "20.0        20\n",
       "22.0        13\n",
       "21.0        13\n",
       "23.0        10\n",
       "24.0         8\n",
       "25.0         7\n",
       "27.0         3\n",
       "29.0         2\n",
       "344.0        1\n",
       "30.0         1\n",
       "Name: OBS_60_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"OBS_60_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DEF_30_CNT_SOCIAL_CIRCLE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     162685\n",
       "1.0      17062\n",
       "2.0       3162\n",
       "3.0        745\n",
       "NaN        639\n",
       "4.0        155\n",
       "5.0         38\n",
       "6.0          9\n",
       "8.0          1\n",
       "34.0         1\n",
       "7.0          1\n",
       "Name: DEF_30_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"DEF_30_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"DEF_30_CNT_SOCIAL_CIRCLE\"].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     163324\n",
       "1.0      17062\n",
       "2.0       3162\n",
       "3.0        745\n",
       "4.0        155\n",
       "5.0         38\n",
       "6.0          9\n",
       "8.0          1\n",
       "34.0         1\n",
       "7.0          1\n",
       "Name: DEF_30_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"DEF_30_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    108631\n",
       "1.0     11266\n",
       "2.0      2161\n",
       "3.0       447\n",
       "NaN       382\n",
       "4.0        98\n",
       "5.0        18\n",
       "6.0         2\n",
       "Name: DEF_30_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"DEF_30_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"DEF_30_CNT_SOCIAL_CIRCLE\"].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     163324\n",
       "1.0      17062\n",
       "2.0       3162\n",
       "3.0        745\n",
       "4.0        155\n",
       "5.0         38\n",
       "6.0          9\n",
       "8.0          1\n",
       "34.0         1\n",
       "7.0          1\n",
       "Name: DEF_30_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"DEF_30_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DEF_60_CNT_SOCIAL_CIRCLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     168340\n",
       "1.0      13167\n",
       "2.0       1871\n",
       "NaN        639\n",
       "3.0        378\n",
       "4.0         84\n",
       "5.0         14\n",
       "6.0          3\n",
       "24.0         1\n",
       "7.0          1\n",
       "Name: DEF_60_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"DEF_60_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"DEF_60_CNT_SOCIAL_CIRCLE\"].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     168979\n",
       "1.0      13167\n",
       "2.0       1871\n",
       "3.0        378\n",
       "4.0         84\n",
       "5.0         14\n",
       "6.0          3\n",
       "24.0         1\n",
       "7.0          1\n",
       "Name: DEF_60_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"DEF_60_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    112373\n",
       "1.0      8674\n",
       "2.0      1299\n",
       "NaN       382\n",
       "3.0       220\n",
       "4.0        51\n",
       "5.0         6\n",
       "Name: DEF_60_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"DEF_60_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"DEF_60_CNT_SOCIAL_CIRCLE\"].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     168979\n",
       "1.0      13167\n",
       "2.0       1871\n",
       "3.0        378\n",
       "4.0         84\n",
       "5.0         14\n",
       "6.0          3\n",
       "24.0         1\n",
       "7.0          1\n",
       "Name: DEF_60_CNT_SOCIAL_CIRCLE, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"DEF_60_CNT_SOCIAL_CIRCLE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AMT_GOODS_PRICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "450000.0     15756\n",
       "225000.0     15142\n",
       "675000.0     14779\n",
       "900000.0      9258\n",
       "270000.0      6757\n",
       "             ...  \n",
       "234342.0         1\n",
       "660244.5         1\n",
       "1180273.5        1\n",
       "225243.0         1\n",
       "744988.5         1\n",
       "Name: AMT_GOODS_PRICE, Length: 793, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"AMT_GOODS_PRICE\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "166"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"AMT_GOODS_PRICE\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.843320e+05\n",
       "mean     5.386932e+05\n",
       "std      3.692258e+05\n",
       "min      4.050000e+04\n",
       "25%      2.385000e+05\n",
       "50%      4.500000e+05\n",
       "75%      6.795000e+05\n",
       "max      4.050000e+06\n",
       "Name: AMT_GOODS_PRICE, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"AMT_GOODS_PRICE\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AMT_GOODS_PRICE', ylabel='Density'>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_data[\"AMT_GOODS_PRICE\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are only 166 null values, we replace them with the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"AMT_GOODS_PRICE\"].fillna(train_data[\"AMT_GOODS_PRICE\"].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"AMT_GOODS_PRICE\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.228940e+05\n",
       "mean     5.379516e+05\n",
       "std      3.697775e+05\n",
       "min      4.500000e+04\n",
       "25%      2.385000e+05\n",
       "50%      4.500000e+05\n",
       "75%      6.795000e+05\n",
       "max      4.050000e+06\n",
       "Name: AMT_GOODS_PRICE, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"AMT_GOODS_PRICE\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"AMT_GOODS_PRICE\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"AMT_GOODS_PRICE\"].fillna(test_data[\"AMT_GOODS_PRICE\"].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.230050e+05\n",
       "mean     5.379516e+05\n",
       "std      3.696106e+05\n",
       "min      4.500000e+04\n",
       "25%      2.385000e+05\n",
       "50%      4.500000e+05\n",
       "75%      6.795000e+05\n",
       "max      4.050000e+06\n",
       "Name: AMT_GOODS_PRICE, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"AMT_GOODS_PRICE\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"AMT_GOODS_PRICE\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NAME_TYPE_SUITE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unaccompanied      149052\n",
       "Family              24044\n",
       "Spouse, partner      6869\n",
       "Children             1982\n",
       "Other_B              1081\n",
       "NaN                   769\n",
       "Other_A               528\n",
       "Group of people       173\n",
       "Name: NAME_TYPE_SUITE, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"NAME_TYPE_SUITE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"NAME_TYPE_SUITE\"].fillna(\"Unaccompanied\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unaccompanied      149821\n",
       "Family              24044\n",
       "Spouse, partner      6869\n",
       "Children             1982\n",
       "Other_B              1081\n",
       "Other_A               528\n",
       "Group of people       173\n",
       "Name: NAME_TYPE_SUITE, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"NAME_TYPE_SUITE\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unaccompanied      99467\n",
       "Family             16105\n",
       "Spouse, partner     4501\n",
       "Children            1285\n",
       "Other_B              689\n",
       "NaN                  522\n",
       "Other_A              338\n",
       "Group of people       98\n",
       "Name: NAME_TYPE_SUITE, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"NAME_TYPE_SUITE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"NAME_TYPE_SUITE\"].fillna(\"Unaccompanied\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unaccompanied      99989\n",
       "Family             16105\n",
       "Spouse, partner     4501\n",
       "Children            1285\n",
       "Other_B              689\n",
       "Other_A              338\n",
       "Group of people       98\n",
       "Name: NAME_TYPE_SUITE, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"NAME_TYPE_SUITE\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EXT_SOURCE_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    147845.000000\n",
       "mean          0.510898\n",
       "std           0.194895\n",
       "min           0.000527\n",
       "25%           0.370650\n",
       "50%           0.535276\n",
       "75%           0.669057\n",
       "max           0.893976\n",
       "Name: EXT_SOURCE_3, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"EXT_SOURCE_3\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EXT_SOURCE_3', ylabel='Density'>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_data[\"EXT_SOURCE_3\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36653"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"EXT_SOURCE_3\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"EXT_SOURCE_3\"].fillna(train_data[\"EXT_SOURCE_3\"].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"EXT_SOURCE_3\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    184498.000000\n",
       "mean          0.515741\n",
       "std           0.174736\n",
       "min           0.000527\n",
       "25%           0.417100\n",
       "50%           0.535276\n",
       "75%           0.636376\n",
       "max           0.893976\n",
       "Name: EXT_SOURCE_3, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"EXT_SOURCE_3\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    98696.000000\n",
       "mean         0.510791\n",
       "std          0.194771\n",
       "min          0.000527\n",
       "25%          0.370650\n",
       "50%          0.535276\n",
       "75%          0.669057\n",
       "max          0.896010\n",
       "Name: EXT_SOURCE_3, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"EXT_SOURCE_3\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24309"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"EXT_SOURCE_3\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"EXT_SOURCE_3\"].fillna(test_data[\"EXT_SOURCE_3\"].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"EXT_SOURCE_3\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EXT_SOURCE_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    184086.000000\n",
       "mean          0.514613\n",
       "std           0.190880\n",
       "min           0.000001\n",
       "25%           0.392422\n",
       "50%           0.565996\n",
       "75%           0.663798\n",
       "max           0.855000\n",
       "Name: EXT_SOURCE_2, dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"EXT_SOURCE_2\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EXT_SOURCE_2', ylabel='Density'>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_data[\"EXT_SOURCE_2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"EXT_SOURCE_2\"].fillna(train_data[\"EXT_SOURCE_2\"].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"EXT_SOURCE_2\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.227580e+05\n",
       "mean     5.140578e-01\n",
       "std      1.913316e-01\n",
       "min      8.173617e-08\n",
       "25%      3.924913e-01\n",
       "50%      5.658298e-01\n",
       "75%      6.632936e-01\n",
       "max      8.549997e-01\n",
       "Name: EXT_SOURCE_2, dtype: float64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"EXT_SOURCE_2\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "247"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"EXT_SOURCE_2\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"EXT_SOURCE_2\"].fillna(test_data[\"EXT_SOURCE_2\"].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"EXT_SOURCE_2\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have no null values in our dataset and we can proceed to do further preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isna().sum().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_target=train_data[\"TARGET\"]\n",
    "train_data.drop(axis=1,columns=\"TARGET\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data=pd.concat([train_data,test_data],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>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>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</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>2.000000</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</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>3.000000</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</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>1.000000</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</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",
       "    </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</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",
       "    </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>123000</th>\n",
       "      <td>128638</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>117000.0</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>32602.5</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123001</th>\n",
       "      <td>169821</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>1006920.0</td>\n",
       "      <td>42790.5</td>\n",
       "      <td>900000.0</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>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123002</th>\n",
       "      <td>442166</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>153000.0</td>\n",
       "      <td>625536.0</td>\n",
       "      <td>26851.5</td>\n",
       "      <td>540000.0</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>1.903676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123003</th>\n",
       "      <td>301605</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>1089000.0</td>\n",
       "      <td>31972.5</td>\n",
       "      <td>1089000.0</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>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123004</th>\n",
       "      <td>176833</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>495882.0</td>\n",
       "      <td>36211.5</td>\n",
       "      <td>405000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>307503 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0           450407         Cash loans           F            N   \n",
       "1           271298         Cash loans           M            Y   \n",
       "2           122238         Cash loans           M            Y   \n",
       "3           305311         Cash loans           M            N   \n",
       "4           414121         Cash loans           F            N   \n",
       "...            ...                ...         ...          ...   \n",
       "123000      128638         Cash loans           F            Y   \n",
       "123001      169821         Cash loans           F            N   \n",
       "123002      442166         Cash loans           F            N   \n",
       "123003      301605         Cash loans           M            N   \n",
       "123004      176833         Cash loans           M            Y   \n",
       "\n",
       "       FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
       "0                    Y             1           67500.0    227520.0   \n",
       "1                    Y             1          247500.0   1882372.5   \n",
       "2                    Y             1          180000.0    101880.0   \n",
       "3                    N             0           81000.0    405000.0   \n",
       "4                    Y             0          157500.0    888840.0   \n",
       "...                ...           ...               ...         ...   \n",
       "123000               Y             1          117000.0    675000.0   \n",
       "123001               Y             0          225000.0   1006920.0   \n",
       "123002               Y             0          153000.0    625536.0   \n",
       "123003               Y             0          180000.0   1089000.0   \n",
       "123004               Y             0          135000.0    495882.0   \n",
       "\n",
       "        AMT_ANNUITY  AMT_GOODS_PRICE  ... FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
       "0           11065.5         180000.0  ...                0                0   \n",
       "1           65560.5        1719000.0  ...                0                0   \n",
       "2           10827.0          90000.0  ...                0                0   \n",
       "3           20677.5         405000.0  ...                0                0   \n",
       "4           29506.5         675000.0  ...                0                0   \n",
       "...             ...              ...  ...              ...              ...   \n",
       "123000      32602.5         675000.0  ...                0                0   \n",
       "123001      42790.5         900000.0  ...                0                0   \n",
       "123002      26851.5         540000.0  ...                0                0   \n",
       "123003      31972.5        1089000.0  ...                0                0   \n",
       "123004      36211.5         405000.0  ...                0                0   \n",
       "\n",
       "       FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                     0                0                        0.0   \n",
       "1                     0                0                        0.0   \n",
       "2                     0                0                        0.0   \n",
       "3                     0                0                        0.0   \n",
       "4                     0                0                        0.0   \n",
       "...                 ...              ...                        ...   \n",
       "123000                0                0                        0.0   \n",
       "123001                0                0                        0.0   \n",
       "123002                0                0                        0.0   \n",
       "123003                0                0                        0.0   \n",
       "123004                0                0                        0.0   \n",
       "\n",
       "        AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                             0.0                         0.0   \n",
       "1                             0.0                         0.0   \n",
       "2                             0.0                         0.0   \n",
       "3                             0.0                         0.0   \n",
       "4                             0.0                         0.0   \n",
       "...                           ...                         ...   \n",
       "123000                        0.0                         0.0   \n",
       "123001                        0.0                         0.0   \n",
       "123002                        0.0                         0.0   \n",
       "123003                        0.0                         0.0   \n",
       "123004                        0.0                         0.0   \n",
       "\n",
       "        AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                             1.0                        0.0   \n",
       "1                             0.0                        1.0   \n",
       "2                             2.0                        0.0   \n",
       "3                             0.0                        0.0   \n",
       "4                             0.0                        0.0   \n",
       "...                           ...                        ...   \n",
       "123000                        0.0                        3.0   \n",
       "123001                        0.0                        2.0   \n",
       "123002                        0.0                        0.0   \n",
       "123003                        0.0                        1.0   \n",
       "123004                        0.0                        0.0   \n",
       "\n",
       "        AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                         2.000000  \n",
       "1                         3.000000  \n",
       "2                         1.000000  \n",
       "3                         2.000000  \n",
       "4                         2.000000  \n",
       "...                            ...  \n",
       "123000                    1.000000  \n",
       "123001                    0.000000  \n",
       "123002                    1.903676  \n",
       "123003                    2.000000  \n",
       "123004                    4.000000  \n",
       "\n",
       "[307503 rows x 72 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_with_numerical_data=list(combined_data.select_dtypes(['int64','float64']).columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for col in columns_with_numerical_data:\n",
    "# \tquart1=combined_data[col].quantile(0.25)\n",
    "# \tquart3=combined_data[col].quantile(0.75)\n",
    "# \tiqr=quart3-quart1\n",
    "# \tupper_whisker=quart3+iqr*1.5\n",
    "# \tlower_whisker=quart1-iqr*1.5\n",
    "# \tcombined_data.loc[combined_data[col]>upper_whisker,col]=upper_whisker\n",
    "# \tcombined_data.loc[combined_data[col]<lower_whisker,col]=lower_whisker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>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>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>DAYS_REGISTRATION</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>count</th>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>3.075030e+05</td>\n",
       "      <td>3.075030e+05</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>3.075030e+05</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "      <td>307503.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>278182.095986</td>\n",
       "      <td>0.417063</td>\n",
       "      <td>1.687969e+05</td>\n",
       "      <td>5.990259e+05</td>\n",
       "      <td>27108.512205</td>\n",
       "      <td>5.383966e+05</td>\n",
       "      <td>0.020868</td>\n",
       "      <td>-16037.082916</td>\n",
       "      <td>63816.743118</td>\n",
       "      <td>-4986.141000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008127</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>0.005538</td>\n",
       "      <td>0.006055</td>\n",
       "      <td>0.029720</td>\n",
       "      <td>0.231289</td>\n",
       "      <td>0.229634</td>\n",
       "      <td>1.899922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>102790.034460</td>\n",
       "      <td>0.722128</td>\n",
       "      <td>2.371255e+05</td>\n",
       "      <td>4.024909e+05</td>\n",
       "      <td>14493.641361</td>\n",
       "      <td>3.692797e+05</td>\n",
       "      <td>0.013831</td>\n",
       "      <td>4363.969621</td>\n",
       "      <td>141277.212245</td>\n",
       "      <td>3522.894235</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089782</td>\n",
       "      <td>0.024388</td>\n",
       "      <td>0.022518</td>\n",
       "      <td>0.018299</td>\n",
       "      <td>0.078015</td>\n",
       "      <td>0.103038</td>\n",
       "      <td>0.190722</td>\n",
       "      <td>0.856817</td>\n",
       "      <td>0.744067</td>\n",
       "      <td>1.738460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100002.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.565000e+04</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>1615.500000</td>\n",
       "      <td>4.050000e+04</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>-25229.000000</td>\n",
       "      <td>-17912.000000</td>\n",
       "      <td>-24672.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>189149.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.125000e+05</td>\n",
       "      <td>2.700000e+05</td>\n",
       "      <td>16524.000000</td>\n",
       "      <td>2.385000e+05</td>\n",
       "      <td>0.010006</td>\n",
       "      <td>-19682.000000</td>\n",
       "      <td>-2760.000000</td>\n",
       "      <td>-7479.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>278204.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.471500e+05</td>\n",
       "      <td>5.135310e+05</td>\n",
       "      <td>24903.000000</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>0.018850</td>\n",
       "      <td>-15750.000000</td>\n",
       "      <td>-1213.000000</td>\n",
       "      <td>-4504.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.897419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>367143.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.025000e+05</td>\n",
       "      <td>8.086500e+05</td>\n",
       "      <td>34596.000000</td>\n",
       "      <td>6.795000e+05</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>-12413.000000</td>\n",
       "      <td>-289.000000</td>\n",
       "      <td>-2010.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>456255.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.170000e+08</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>258025.500000</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>0.072508</td>\n",
       "      <td>-7489.000000</td>\n",
       "      <td>365243.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          SK_ID_CURR   CNT_CHILDREN  AMT_INCOME_TOTAL    AMT_CREDIT  \\\n",
       "count  307503.000000  307503.000000      3.075030e+05  3.075030e+05   \n",
       "mean   278182.095986       0.417063      1.687969e+05  5.990259e+05   \n",
       "std    102790.034460       0.722128      2.371255e+05  4.024909e+05   \n",
       "min    100002.000000       0.000000      2.565000e+04  4.500000e+04   \n",
       "25%    189149.500000       0.000000      1.125000e+05  2.700000e+05   \n",
       "50%    278204.000000       0.000000      1.471500e+05  5.135310e+05   \n",
       "75%    367143.500000       1.000000      2.025000e+05  8.086500e+05   \n",
       "max    456255.000000      19.000000      1.170000e+08  4.050000e+06   \n",
       "\n",
       "         AMT_ANNUITY  AMT_GOODS_PRICE  REGION_POPULATION_RELATIVE  \\\n",
       "count  307503.000000     3.075030e+05               307503.000000   \n",
       "mean    27108.512205     5.383966e+05                    0.020868   \n",
       "std     14493.641361     3.692797e+05                    0.013831   \n",
       "min      1615.500000     4.050000e+04                    0.000290   \n",
       "25%     16524.000000     2.385000e+05                    0.010006   \n",
       "50%     24903.000000     4.500000e+05                    0.018850   \n",
       "75%     34596.000000     6.795000e+05                    0.028663   \n",
       "max    258025.500000     4.050000e+06                    0.072508   \n",
       "\n",
       "          DAYS_BIRTH  DAYS_EMPLOYED  DAYS_REGISTRATION  ...  FLAG_DOCUMENT_18  \\\n",
       "count  307503.000000  307503.000000      307503.000000  ...     307503.000000   \n",
       "mean   -16037.082916   63816.743118       -4986.141000  ...          0.008127   \n",
       "std      4363.969621  141277.212245        3522.894235  ...          0.089782   \n",
       "min    -25229.000000  -17912.000000      -24672.000000  ...          0.000000   \n",
       "25%    -19682.000000   -2760.000000       -7479.500000  ...          0.000000   \n",
       "50%    -15750.000000   -1213.000000       -4504.000000  ...          0.000000   \n",
       "75%    -12413.000000    -289.000000       -2010.000000  ...          0.000000   \n",
       "max     -7489.000000  365243.000000           0.000000  ...          1.000000   \n",
       "\n",
       "       FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  \\\n",
       "count     307503.000000     307503.000000     307503.000000   \n",
       "mean           0.000595          0.000507          0.000335   \n",
       "std            0.024388          0.022518          0.018299   \n",
       "min            0.000000          0.000000          0.000000   \n",
       "25%            0.000000          0.000000          0.000000   \n",
       "50%            0.000000          0.000000          0.000000   \n",
       "75%            0.000000          0.000000          0.000000   \n",
       "max            1.000000          1.000000          1.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "count               307503.000000              307503.000000   \n",
       "mean                     0.005538                   0.006055   \n",
       "std                      0.078015                   0.103038   \n",
       "min                      0.000000                   0.000000   \n",
       "25%                      0.000000                   0.000000   \n",
       "50%                      0.000000                   0.000000   \n",
       "75%                      0.000000                   0.000000   \n",
       "max                      4.000000                   9.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "count               307503.000000              307503.000000   \n",
       "mean                     0.029720                   0.231289   \n",
       "std                      0.190722                   0.856817   \n",
       "min                      0.000000                   0.000000   \n",
       "25%                      0.000000                   0.000000   \n",
       "50%                      0.000000                   0.000000   \n",
       "75%                      0.000000                   0.000000   \n",
       "max                      8.000000                  27.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "count              307503.000000               307503.000000  \n",
       "mean                    0.229634                    1.899922  \n",
       "std                     0.744067                    1.738460  \n",
       "min                     0.000000                    0.000000  \n",
       "25%                     0.000000                    1.000000  \n",
       "50%                     0.000000                    1.897419  \n",
       "75%                     0.000000                    3.000000  \n",
       "max                   261.000000                   25.000000  \n",
       "\n",
       "[8 rows x 60 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns with categorical values have to be encoded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_encode=list(train_data.select_dtypes(include=['object']).columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test data has a column in the columns to be encoded whose value is indifferent from the train data column. So we combine and split with randomising."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data_encoded=pd.get_dummies(combined_data,columns=columns_to_encode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_encoded=combined_data_encoded.iloc[:train_data.shape[0],:]\n",
    "test_data_encoded=combined_data_encoded.iloc[train_data.shape[0]:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(184498, 185)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_encoded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_encoded=pd.concat([train_data_encoded,train_data_target],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>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>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>DAYS_REGISTRATION</th>\n",
       "      <th>...</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 5</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 6</th>\n",
       "      <th>ORGANIZATION_TYPE_Trade: type 7</th>\n",
       "      <th>ORGANIZATION_TYPE_Transport: type 1</th>\n",
       "      <th>ORGANIZATION_TYPE_Transport: type 2</th>\n",
       "      <th>ORGANIZATION_TYPE_Transport: type 3</th>\n",
       "      <th>ORGANIZATION_TYPE_Transport: type 4</th>\n",
       "      <th>ORGANIZATION_TYPE_University</th>\n",
       "      <th>ORGANIZATION_TYPE_XNA</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>450407</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>0.018634</td>\n",
       "      <td>-17210</td>\n",
       "      <td>-774</td>\n",
       "      <td>-5580.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>271298</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>0.018850</td>\n",
       "      <td>-18640</td>\n",
       "      <td>-96</td>\n",
       "      <td>-5711.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>122238</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>0.022625</td>\n",
       "      <td>-14649</td>\n",
       "      <td>-6130</td>\n",
       "      <td>-1776.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>305311</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>0.035792</td>\n",
       "      <td>-12762</td>\n",
       "      <td>-5891</td>\n",
       "      <td>-6430.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>414121</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>0.018801</td>\n",
       "      <td>-16329</td>\n",
       "      <td>-345</td>\n",
       "      <td>-1413.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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>184501</th>\n",
       "      <td>439663</td>\n",
       "      <td>1</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>143910.0</td>\n",
       "      <td>15498.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>0.016612</td>\n",
       "      <td>-12139</td>\n",
       "      <td>-1442</td>\n",
       "      <td>-3888.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184502</th>\n",
       "      <td>385689</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>360000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>360000.0</td>\n",
       "      <td>0.010032</td>\n",
       "      <td>-22472</td>\n",
       "      <td>365243</td>\n",
       "      <td>-12171.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184503</th>\n",
       "      <td>153567</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>900000.0</td>\n",
       "      <td>45954.0</td>\n",
       "      <td>900000.0</td>\n",
       "      <td>0.014464</td>\n",
       "      <td>-15037</td>\n",
       "      <td>-88</td>\n",
       "      <td>-5025.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184504</th>\n",
       "      <td>365526</td>\n",
       "      <td>0</td>\n",
       "      <td>123300.0</td>\n",
       "      <td>744988.5</td>\n",
       "      <td>20484.0</td>\n",
       "      <td>744988.5</td>\n",
       "      <td>0.026392</td>\n",
       "      <td>-20586</td>\n",
       "      <td>365243</td>\n",
       "      <td>-6310.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184505</th>\n",
       "      <td>125605</td>\n",
       "      <td>0</td>\n",
       "      <td>81000.0</td>\n",
       "      <td>167895.0</td>\n",
       "      <td>17757.0</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>0.018850</td>\n",
       "      <td>-16397</td>\n",
       "      <td>-3009</td>\n",
       "      <td>-708.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184498 rows × 186 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SK_ID_CURR  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0           450407             1           67500.0    227520.0      11065.5   \n",
       "1           271298             1          247500.0   1882372.5      65560.5   \n",
       "2           122238             1          180000.0    101880.0      10827.0   \n",
       "3           305311             0           81000.0    405000.0      20677.5   \n",
       "4           414121             0          157500.0    888840.0      29506.5   \n",
       "...            ...           ...               ...         ...          ...   \n",
       "184501      439663             1          135000.0    143910.0      15498.0   \n",
       "184502      385689             0          202500.0    360000.0      18000.0   \n",
       "184503      153567             0          135000.0    900000.0      45954.0   \n",
       "184504      365526             0          123300.0    744988.5      20484.0   \n",
       "184505      125605             0           81000.0    167895.0      17757.0   \n",
       "\n",
       "        AMT_GOODS_PRICE  REGION_POPULATION_RELATIVE  DAYS_BIRTH  \\\n",
       "0              180000.0                    0.018634      -17210   \n",
       "1             1719000.0                    0.018850      -18640   \n",
       "2               90000.0                    0.022625      -14649   \n",
       "3              405000.0                    0.035792      -12762   \n",
       "4              675000.0                    0.018801      -16329   \n",
       "...                 ...                         ...         ...   \n",
       "184501         135000.0                    0.016612      -12139   \n",
       "184502         360000.0                    0.010032      -22472   \n",
       "184503         900000.0                    0.014464      -15037   \n",
       "184504         744988.5                    0.026392      -20586   \n",
       "184505         157500.0                    0.018850      -16397   \n",
       "\n",
       "        DAYS_EMPLOYED  DAYS_REGISTRATION  ...  \\\n",
       "0                -774            -5580.0  ...   \n",
       "1                 -96            -5711.0  ...   \n",
       "2               -6130            -1776.0  ...   \n",
       "3               -5891            -6430.0  ...   \n",
       "4                -345            -1413.0  ...   \n",
       "...               ...                ...  ...   \n",
       "184501          -1442            -3888.0  ...   \n",
       "184502         365243           -12171.0  ...   \n",
       "184503            -88            -5025.0  ...   \n",
       "184504         365243            -6310.0  ...   \n",
       "184505          -3009             -708.0  ...   \n",
       "\n",
       "        ORGANIZATION_TYPE_Trade: type 5  ORGANIZATION_TYPE_Trade: type 6  \\\n",
       "0                                     0                                0   \n",
       "1                                     0                                0   \n",
       "2                                     0                                0   \n",
       "3                                     0                                0   \n",
       "4                                     0                                0   \n",
       "...                                 ...                              ...   \n",
       "184501                                0                                0   \n",
       "184502                                0                                0   \n",
       "184503                                0                                0   \n",
       "184504                                0                                0   \n",
       "184505                                0                                0   \n",
       "\n",
       "        ORGANIZATION_TYPE_Trade: type 7  ORGANIZATION_TYPE_Transport: type 1  \\\n",
       "0                                     0                                    0   \n",
       "1                                     0                                    0   \n",
       "2                                     0                                    0   \n",
       "3                                     0                                    0   \n",
       "4                                     0                                    0   \n",
       "...                                 ...                                  ...   \n",
       "184501                                0                                    0   \n",
       "184502                                0                                    0   \n",
       "184503                                0                                    0   \n",
       "184504                                0                                    0   \n",
       "184505                                0                                    0   \n",
       "\n",
       "        ORGANIZATION_TYPE_Transport: type 2  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "...                                     ...   \n",
       "184501                                    0   \n",
       "184502                                    0   \n",
       "184503                                    0   \n",
       "184504                                    0   \n",
       "184505                                    0   \n",
       "\n",
       "        ORGANIZATION_TYPE_Transport: type 3  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "...                                     ...   \n",
       "184501                                    0   \n",
       "184502                                    0   \n",
       "184503                                    0   \n",
       "184504                                    0   \n",
       "184505                                    0   \n",
       "\n",
       "        ORGANIZATION_TYPE_Transport: type 4  ORGANIZATION_TYPE_University  \\\n",
       "0                                         0                             0   \n",
       "1                                         0                             0   \n",
       "2                                         1                             0   \n",
       "3                                         0                             0   \n",
       "4                                         0                             0   \n",
       "...                                     ...                           ...   \n",
       "184501                                    0                             0   \n",
       "184502                                    0                             0   \n",
       "184503                                    0                             0   \n",
       "184504                                    0                             0   \n",
       "184505                                    0                             0   \n",
       "\n",
       "        ORGANIZATION_TYPE_XNA  TARGET  \n",
       "0                           0       0  \n",
       "1                           0       0  \n",
       "2                           0       0  \n",
       "3                           0       0  \n",
       "4                           0       0  \n",
       "...                       ...     ...  \n",
       "184501                      0       0  \n",
       "184502                      1       0  \n",
       "184503                      0       0  \n",
       "184504                      1       0  \n",
       "184505                      0       0  \n",
       "\n",
       "[184498 rows x 186 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(123005, 185)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_encoded.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for duplicates after dropping columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_encoded.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No duplicates found."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we do further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_with_number_uniques=train_data_encoded.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to check for skews first. Most of our columns are categorical or has less number of unique values. So we have only few columns to check skews for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AMT_INCOME_TOTAL'>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train_data_encoded[\"AMT_INCOME_TOTAL\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_encoded=train_data_encoded[train_data_encoded[\"AMT_INCOME_TOTAL\"]<=1e6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AMT_INCOME_TOTAL'>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cDOhbJW22vVnSCRW/8fC+iPgv278m6a9sL1PxbH1z9UW8yv1un3pZxQ7bJ8vTeyOi1Y87GpS0XNK+8n6elXRlRBybYfwvq3j1/Hjlsn+Q9CeTv6Yn6X7bJ8rTOyPimhbnhC7GoSwBIBH7hAEgEbsj0Da2PyRp6n+lPx0RH8mYTx2279CL9+NtiYi/zpgPFh92RwBAInZHAEAiIgwAiYgwACQiwgCQ6P8BE0PsaHBDoaoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train_data_encoded[\"AMT_INCOME_TOTAL\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AMT_CREDIT'>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train_data_encoded[\"AMT_CREDIT\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_encoded[train_data_encoded[\"AMT_CREDIT\"]>3e6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_encoded=train_data_encoded[train_data_encoded[\"AMT_CREDIT\"]<=3e6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AMT_CREDIT', ylabel='Density'>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_data_encoded[\"AMT_CREDIT\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AMT_ANNUITY', ylabel='Density'>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_data_encoded[\"AMT_ANNUITY\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AMT_GOODS_PRICE', ylabel='Density'>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_data_encoded[\"AMT_GOODS_PRICE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AMT_GOODS_PRICE'>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEHCAYAAACQkJyuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAASsUlEQVR4nO3df5DU9X3H8dcbThTETBqwxoB1Tc5qIvgLxto6zYBKvIJTqyONTlLQZNJpxgCaqmkK8SDFSRr8BWgnY9Wg06Q2TJgkgydWo45OW5PeKQQQoqtcUjFWc0YTEGtO3v3j+93L3rI/vrt7u+8LPB8zN+z3+/189vPez92++N53dz9n7i4AQPuNiS4AAA5VBDAABCGAASAIAQwAQQhgAAjSUU/jyZMney6Xa1EpAHBw6uvr+4W7H126v64AzuVy6u3tHbmqAOAQYGY/LbefSxAAEIQABoAgBDAABCGAASAIAQwAQQhgAAhCAANAEAIYAIIQwAAQhAAGgCAEMAAEIYABIAgBDABBCGAACEIAA0AQAhgAghDAABCEAAaAIAQwAASp62/CRVi7dq3y+XzD/Xfv3i1JmjJlSlN1dHZ2atGiRU3dBwAUG/UBnM/ntXnbDr074X0N9R/71puSpFf+r/GHOvat1xvuCwCVjPoAlqR3J7xP+06e21Df8Tt7JKnh/sX3AQAjiWvAABCEAAaAIAQwAAQhgAEgCAEMAEEIYAAIQgADQBACGACCEMAAEIQABoAgBDAABCGAASAIAQwAQQhgAAhCAANAEAIYAIIQwAAQhAAGgCAEMAAEIYABIAgBDABBCGAACEIAA0AQAhgAghDAABCEAAaAIAQwAAQhgAEgCAEMAEEIYAAIQgADQBACGACCEMAAEIQABoAgBDAABCGAASAIAQwAQQhgAAhCAANAEAIYAIIQwAAQpC0BvHbtWq1du7YdQ6EGvhfA6NHRjkHy+Xw7hkEGfC+A0YNLEAAQhAAGgCAEMAAEIYABIAgBDABBCGAACEIAA0AQAhgAghDAABCEAAaAIAQwAAQhgAEgCAEMAEEIYAAIQgADQBACGACCEMAAEIQABoAgBDAABCGAASAIAQwAQQhgAAhCAANAEAIYAIIQwAAQhAAGgCAEMAAEIYABIAgBDABBCGAACEIAA0AQAhgAghDAABCEAAaAIAQwAAQhgAEgCAEMAEEIYAAI0hFdANpry5YtkqRZs2bFFtJCc+fOVU9PT9lj06ZN07Zt24a2x40bp3feeWdo++abb9YNN9yg1atXq7OzU5I0MDCgFStWaPHixVqzZo26u7s1adKkYcfOO+883Xrrreru7tbs2bOHHStuX82aNWu0YcMGzZ8/XxdccIGWLFkyrI4s8vl8pn7Vaiuu46qrrso8diN1VKtr165duv7667Vq1SrNmDGj7scxUnp7e2vW0SjOgHHQqRS+koaFr6Rh4StJ3d3d2rt3r1auXDm0795779XWrVu1cuVKbd26Vffdd98Bx2677TZJ0o033njAseL21WzYsEGStH79eq1cufKAOrLI2q9abcV1NKrR+ovrWr58ufbv36/u7u5M7VslSx2NIoAPIQfzWe9I2bNnjySpv79f+XxeAwMD2rRpk9xd/f39cndt2rRJAwMDw465uyRpcHBQjz322LBjhfbVrFmzZth2f3//sDqyyOfzmfpVq620jjvuuCPT2I3UUa2uBx54YOh7sWfPHvX19dX1OEZKb29vzTqaYYUfnCxmzpzpvb29dQ9y6aWXat++fXX9KlKQz+f163dce0+/rO6+kjR+Z3I2tO/kuQ31l6QjN9+vo8ZZQ/WPJoXLD8gml8vp1FNPVU9PjwYHB4f2d3R0aN68eXL3A44VjhcugwwODg61v+aaayqOVe0/x1wup3Xr1tWs94orrhgKvmr9brnlloq1lavj8ccfrzl2I3VUq6vUxIkTtXHjxorts8xxIy688MKhAK5URxZm1ufuM0v31zwDNrO/NrNeM+t97bXX6h4Y+F3V39+vRx555IBAGBwc1MMPP1z2WOF48bFC+2bqaKRdpX4jWVszdVSrq1RxCJZr34rHUW7ccnU0o+aLcO5+p6Q7peQMuJFBpkyZIklavXp13X2XLFmivhf/t5FhR8z+I96jzg8e01D9owmXIOpT7Qx4zpw5Vc+Azz///GFnZ3PmzGmqjqztSs88yxnJ2pqpo1pdpSZOnFi1fSseR2Hc0jPgkcQ1YKCCZcuWaeHChRozZvjTZOzYsVqwYEHZY5K0dOnSYccK7au55JJLqtaRtd4s/arVVlrH/PnzM43dSB3V6uroGH5uuGLFiqrts8xxI5YvX16zjmYQwIeQeq/lHYoKZzi5XE6dnZ2aNGmSurq6ZGbK5XIyM3V1dWnSpEnDjpmZpCQ4Zs+ePexYoX01ixcvHrZdOGss1JFFZ2dnpn7Vaiuto5G3oWWto1pd8+bNG/peTJw4sezbv+qd40bMnDmzZh3NIIBx0Jk7t/ILrtOmTRu2PW7cuGHbK1as0JFHHjnsrG3hwoWaPn26li1bpunTpw870yocu/rqqyUlZ7+lx7KemRXOPufPn69ly5YdUEcWWftVq624jkY1Wn9xXcuXL9eYMWOqnnXWO8eNyFJHo9ryLoglS5ZIau4acKPvYhiJd0GM39mjGQfBNWCpue8FgMY0/C4IAEBrEMAAEIQABoAgBDAABCGAASAIAQwAQQhgAAhCAANAEAIYAIIQwAAQhAAGgCAEMAAEIYABIAgBDABBCGAACEIAA0AQAhgAghDAABCEAAaAIAQwAAQhgAEgCAEMAEEIYAAIQgADQBACGACCEMAAEIQABoAgBDAABCGAASAIAQwAQQhgAAhCAANAEAIYAIIQwAAQhAAGgCAEMAAEIYABIAgBDABBOtoxSGdnZzuGQQZ8L4DRoy0BvGjRonYMgwz4XgCjB5cgACAIAQwAQQhgAAhCAANAEAIYAIIQwAAQhAAGgCAEMAAEIYABIAgBDABBCGAACEIAA0AQAhgAghDAABCEAAaAIAQwAAQhgAEgCAEMAEEIYAAIQgADQBACGACCEMAAEIQABoAgBDAABCGAASAIAQwAQQhgAAhCAANAEAIYAIIQwAAQhAAGgCAEMAAEIYABIAgBDABBCGAACEIAA0AQAhgAghDAABCEAAaAIAQwAATpiC4gi7Fvva7xO3sa7DsgSQ33L4wvHdNwfwAoZ9QHcGdnZ1P9d+8elCRNmdJMgB7TdB0AUGrUB/CiRYuiSwCAluAaMAAEIYABIAgBDABBCGAACEIAA0AQAhgAghDAABCEAAaAIAQwAAQhgAEgCAEMAEEIYAAIQgADQBACGACCEMAAEIQABoAgBDAABCGAASAIAQwAQQhgAAhi7p69sdlrkn5ax/1PlvSLeotqA+qqD3XVh7rqMxrrGumajnf3o0t31hXA9TKzXnef2bIBGkRd9aGu+lBXfUZjXe2qiUsQABCEAAaAIK0O4DtbfP+Noq76UFd9qKs+o7GuttTU0mvAAIDKuAQBAEEIYAAI0nQAm1mXmf3EzPJm9ndljpuZrUmP/9jMzmx2zBGqa5aZvWlmm9OvG9pU1z1m9qqZbatwPGq+atXV9vkys+PM7DEz22Fm281sSZk2bZ+vjHVFzNcRZvYjM9uS1rWiTJuI+cpSV8jzMR17rJk9Y2Ybyxxr7Xy5e8NfksZKekHSByWNk7RF0kdK2syV9KAkk3S2pB82M+YI1jVL0sZW11Kmto9KOlPStgrH2z5fGetq+3xJOlbSmentoyQ9N0p+vrLUFTFfJmlievswST+UdPYomK8sdYU8H9OxPy/pW+XGb/V8NXsGfJakvLu/6O7vSLpf0kUlbS6SdJ8nnpL0XjM7tslxR6KuEO7+hKTXqzSJmK8sdbWdu//c3Z9Ob/9a0g5JU0qatX2+MtbVdukc7Ek3D0u/Sl9lj5ivLHWFMLOpkuZJuqtCk5bOV7MBPEXS/xRtv6QDfxCztBlpWcf84/TXogfN7JQW15RVxHxlFTZfZpaTdIaSs6diofNVpS4pYL7SX6c3S3pV0sPuPirmK0NdUszP122Srpe0v8Lxls5XswFsZfaV/s+Wpc1IyzLm00o+n32apLWSvtvimrKKmK8swubLzCZK+o6kq939V6WHy3Rpy3zVqCtkvtz9XXc/XdJUSWeZ2bSSJiHzlaGuts+XmV0o6VV376vWrMy+EZuvZgP4JUnHFW1PlfRyA21GWs0x3f1XhV+L3L1H0mFmNrnFdWURMV81Rc2XmR2mJOS+6e4byjQJma9adUX/fLn7G5Iel9RVcij056tSXUHzdY6kPzezfiWXKc81s38padPS+Wo2gP9b0olmdoKZjZN0maTvl7T5vqQF6auJZ0t6091/3uS4TddlZu83M0tvn6VkLgZaXFcWEfNVU8R8pePdLWmHu99SoVnb5ytLXUHzdbSZvTe9PV7S+ZJ2ljSLmK+adUXMl7t/0d2nuntOSUY86u6fLGnW0vnqaKazuw+a2eckPaTknQf3uPt2M/ub9PjXJfUoeSUxL+ktSVc2V/KI1XWppM+a2aCkfZIu8/Rlz1Yys39V8orvZDN7SVK3khclwuYrY10R83WOpL+StDW9fihJfy/pD4rqipivLHVFzNexku41s7FKAuzb7r4x+vmYsa6Q52M57ZwvPooMAEH4JBwABCGAASAIAQwAQQhgAAhCAANABVZjkaoy7f/SzJ61ZNGhb9Vsz7sgAKA8M/uopD1K1oMo/fReadsTJX1b0rnu/ksz+313f7VaH86AMcTMLjYzN7OT0+1cuv0PRW0mm9lvzOx2M1tqv10+8N2i24urjPFJS5b1255+7v+uojfpjzOz28zsBTN73sy+Z8liKYW+U9N9z6dtVqcftClezvAZS5YhfcKSj5oW+p5kZo+n9e0ws4p/cqbkvnaYWXeZ/TvN7KaiPleY2e1F2wvMbFv6OJ81s2vT/evMbFfRXP1nXd8ktFW5RarM7ENmtsnM+szsycLzRdJnJN3h7r9M+1YN38IAfPEld5eS/72flLQ83c4pWdbzmaI2n5W0WdLtJX33ZLj/Lkl9kqak22MlfUrSSen2TUo+YTY23b5S0o+UfB7f0ttXFvW9W9KqdHuWipYTlHS6pH5J56XbD0m6qOj49Cp1Dt2XpCMlPS9pRsn+8Uo+zXVOun1FYU4k/ZmStQ0+kG4fIekz6e11ki6N/l7zVdfzIqeiZVol/UDSientP1LyCTopWb/ia5L+Q9JTkrpq3XdTn4TDwcOShWXOkTRbyccvl6eH9knaYWYz3b1X0seVBPUHGhhmqaRr3X23lCzQIumedPwJSgL3hHS/3P0bZvYpSeem/d92928U+prZNZJ2Fc5Qi7n7ZjP7sqTPKXnCHKvkc/2F41uzFOzue82sT9KHlKzkVdi/L/0UXLmVsb6YPs6X07ZvS/rnLONhdEufJ38iaX36yWlJOjz9t0PSiUr+o54q6Ukzm+bJ+hdlcQkCBX8haZO7PyfpdRu+8v/9ki5LLwe8q8YXIzlFyZlhOZ2SfuYHrirWm/Y7RcnZ85C07c/SvuU8Lanw6+Gtkh61ZKnDawqXPWoxs0lKFuLeXrL/95Q82Z4o021aaa0lVhVdgvhmljowaoyR9Ia7n1709eH02EuSvufuv3H3XZJ+ouRnpOqdAZJ0uZKgVfrv5UXHNkmak+77t5EYzMympwH0gpl9XMklhnKvCBf21zpedpjCjfTM+cOS1is5Q3nKzA6v0E+S/tTMnpH075K+6u7bi/b/WNIrSi5HvFLlPiq5rujJ+4kG+iNI+p/+LjObLw39yaLT0sPfVfIbpCxZye0PJb1Y7f4IYBTO8s6VdJclS/Ndp+RSg0mSJ39VpE/S3ypZgrFR25X82SO5+1ZP1od9UMn11Lyk483sqJI+Z0p6Nu07s6Tu9yhZKvCFCuOdoeSvVSgd82V3v8fdL5I0qORMtZIn3f0Md5/hyaIsxftPlTRdyeIxp1d4nDOq3Dd+R1iySNV/STrJzF4ys09L+oSkT5vZFiXf68Jf23lI0oCZPSvpMSX/0VZd0Y0AhpSsRHWfux/v7jl3P07SLiXXsQpulvSFWj9QNXxF0k3F72xQEr5y972S7pV0iyWrZsnMFkiaIOlRJddxJ6T7lLa5WdI6d3+rdCAzO1XSlyTdkW53WbKGr8zs/ZImSdrd6ANJL9V8RdIXKjzOr6XjyMwOr/bOEIxe7n65ux/r7od5snTl3e6+y9273P00d/+Iu385bevu/vl033R3v7/W/fMiHKTk0sJXS/Z9R8kSi5Kk9Ffw7WqCu/eY2dGSHkwD9A1J25ScOUjJi1c3SXrOzPYreZfBxZ6+xGxmF0v6JzP7kpKTh57iGvXbywYTlLxgttjdf5Ae+5ik1Wb2drp9XYOXD4p9XdK1ZnZCmcd5jKRHLHmlxpW+2JhaZWbLirbPSn/LwCGGD2IAQBAuQQBAEC5BYMSZ2VJJ80t2r3f3GyPqqcTMLpD0jyW7d7n7xRH14NDDJQgACMIlCAAIQgADQBACGACCEMAAEOT/AZVdHURElsYxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train_data_encoded[\"AMT_GOODS_PRICE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_encoded[train_data_encoded[\"AMT_GOODS_PRICE\"]>3e6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_encoded=train_data_encoded[train_data_encoded[\"AMT_GOODS_PRICE\"]<=3e6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DAYS_BIRTH', ylabel='Density'>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_data_encoded[\"DAYS_BIRTH\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DAYS_BIRTH'>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train_data_encoded[\"DAYS_BIRTH\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DAYS_ID_PUBLISH'>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train_data_encoded[\"DAYS_ID_PUBLISH\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DAYS_REGISTRATION'>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train_data_encoded[\"DAYS_REGISTRATION\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EXT_SOURCE_2'>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train_data_encoded[\"EXT_SOURCE_2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AMT_GOODS_PRICE'>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train_data_encoded[\"AMT_GOODS_PRICE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DAYS_LAST_PHONE_CHANGE'>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train_data_encoded[\"DAYS_LAST_PHONE_CHANGE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DAYS_EMPLOYED'>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train_data_encoded[\"DAYS_EMPLOYED\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.heatmap(data=train_data_encoded.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11404"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_encoded[\"DAYS_EMPLOYED\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_target=train_data_encoded[\"TARGET\"]\n",
    "train_data_encoded.drop(axis=1,columns=\"TARGET\",inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "instance_of_standard_scaler=StandardScaler()\n",
    "train_data_standardized=instance_of_standard_scaler.fit_transform(train_data_encoded) \n",
    "test_data_standardized=instance_of_standard_scaler.transform(test_data_encoded)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sampling (Commented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from imblearn.over_sampling import SMOTE\n",
    "# from imblearn.under_sampling import CondensedNearestNeighbour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# smote=SMOTE(random_state=42,sampling_strategy=0.3,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_stand_resampled,train_data_target_resampled=smote.fit_resample(train_data_standardized,train_data_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_target_resampled.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cnn=CondensedNearestNeighbour(n_jobs=-1,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_target_resampled.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_stand_resampled,train_data_target_resampled=cnn.fit_resample(train_data_stand_resampled,train_data_target_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_target_resampled.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_data_split,train_data_split_test,train_data_split_y,train_data_split_test_y=train_test_split(\n",
    "\ttrain_data_standardized,\n",
    "\ttrain_data_target,\n",
    "\t# train_data_stand_resampled,\n",
    "\t# train_data_target_resampled,\n",
    "\ttest_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "id": "dzZrGuwU_srH"
   },
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "from lightgbm import LGBMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "id": "X9eFCMBD_9aD"
   },
   "outputs": [],
   "source": [
    "instance_of_lgbm=LGBMClassifier(verbosity = -1,\n",
    "         feature_pre_filter = False,\n",
    "         objective_type=\"cross-entropy\",\n",
    "         lambda_l1= 0.0007099817067951657,\n",
    "         lambda_l2= 2.2721620492879536e-07,\n",
    "         num_leaves=800,\n",
    "         feature_fraction=0.4,\n",
    "         bagging_fraction= 0.9938087600649544,\n",
    "         bagging_freq=1,\n",
    "         min_child_samples=20,\n",
    "         learning_rate= 0.005,\n",
    "         n_estimators=1300,\n",
    "         class_weight= 'balanced'\n",
    "         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "63kwlkABBHTy",
    "outputId": "936d7834-eb65-4752-b723-5ab89964e224"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n",
      "[LightGBM] [Warning] lambda_l1 is set=0.0007099817067951657, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0007099817067951657\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.9938087600649544, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9938087600649544\n",
      "[LightGBM] [Warning] lambda_l2 is set=2.2721620492879536e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.2721620492879536e-07\n",
      "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(bagging_fraction=0.9938087600649544, bagging_freq=1,\n",
       "               class_weight=&#x27;balanced&#x27;, feature_fraction=0.4,\n",
       "               feature_pre_filter=False, lambda_l1=0.0007099817067951657,\n",
       "               lambda_l2=2.2721620492879536e-07, learning_rate=0.005,\n",
       "               n_estimators=1300, num_leaves=800,\n",
       "               objective_type=&#x27;cross-entropy&#x27;, verbosity=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(bagging_fraction=0.9938087600649544, bagging_freq=1,\n",
       "               class_weight=&#x27;balanced&#x27;, feature_fraction=0.4,\n",
       "               feature_pre_filter=False, lambda_l1=0.0007099817067951657,\n",
       "               lambda_l2=2.2721620492879536e-07, learning_rate=0.005,\n",
       "               n_estimators=1300, num_leaves=800,\n",
       "               objective_type=&#x27;cross-entropy&#x27;, verbosity=-1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier(bagging_fraction=0.9938087600649544, bagging_freq=1,\n",
       "               class_weight='balanced', feature_fraction=0.4,\n",
       "               feature_pre_filter=False, lambda_l1=0.0007099817067951657,\n",
       "               lambda_l2=2.2721620492879536e-07, learning_rate=0.005,\n",
       "               n_estimators=1300, num_leaves=800,\n",
       "               objective_type='cross-entropy', verbosity=-1)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance_of_lgbm.fit(train_data_split,train_data_split_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "executionInfo": {
     "elapsed": 2788,
     "status": "ok",
     "timestamp": 1668521926263,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "QgSYl-rxgz5S"
   },
   "outputs": [],
   "source": [
    "predicted_target_for_train_data_in_lgb=instance_of_lgbm.predict(train_data_split_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 604,
     "status": "ok",
     "timestamp": 1668521930335,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "fQTmMXXEimoq",
    "outputId": "53270b0e-1dd3-445d-d6e7-df29b6090839"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for the train(test) data for lgb is: 0.8736585365853659\n",
      "f1 score for the train(test) data for lgb is: 0.6144469073221698\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy for the train(test) data for lgb is:\",accuracy_score(train_data_split_test_y,predicted_target_for_train_data_in_lgb))\n",
    "print(\"f1 score for the train(test) data for lgb is:\",f1_score(train_data_split_test_y,predicted_target_for_train_data_in_lgb,average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainpred=instance_of_lgbm.predict(train_data_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for the train(train) data for lgb is: 0.9565576769332917\n",
      "f1 score for the train(train) data for lgb is: 0.8821092300367452\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy for the train(train) data for lgb is:\",accuracy_score(train_data_split_y,trainpred))\n",
    "print(\"f1 score for the train(train) data for lgb is:\",f1_score(train_data_split_y,trainpred,average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "instance_for_submission_lgb=LGBMClassifier(verbosity = -1,\n",
    "         feature_pre_filter = False,\n",
    "         objective_type=\"cross-entropy\",\n",
    "         lambda_l1= 0.0007099817067951657,\n",
    "         lambda_l2= 2.2721620492879536e-07,\n",
    "         num_leaves=800,\n",
    "         feature_fraction=0.4,\n",
    "         bagging_fraction= 0.9938087600649544,\n",
    "         bagging_freq=1,\n",
    "         min_child_samples=20,\n",
    "         learning_rate= 0.005,\n",
    "         n_estimators=1300,\n",
    "         class_weight= 'balanced'\n",
    "         )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the full data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n",
      "[LightGBM] [Warning] lambda_l1 is set=0.0007099817067951657, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0007099817067951657\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.9938087600649544, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9938087600649544\n",
      "[LightGBM] [Warning] lambda_l2 is set=2.2721620492879536e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=2.2721620492879536e-07\n",
      "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(bagging_fraction=0.9938087600649544, bagging_freq=1,\n",
       "               class_weight=&#x27;balanced&#x27;, feature_fraction=0.4,\n",
       "               feature_pre_filter=False, lambda_l1=0.0007099817067951657,\n",
       "               lambda_l2=2.2721620492879536e-07, learning_rate=0.005,\n",
       "               n_estimators=1300, num_leaves=800,\n",
       "               objective_type=&#x27;cross-entropy&#x27;, verbosity=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(bagging_fraction=0.9938087600649544, bagging_freq=1,\n",
       "               class_weight=&#x27;balanced&#x27;, feature_fraction=0.4,\n",
       "               feature_pre_filter=False, lambda_l1=0.0007099817067951657,\n",
       "               lambda_l2=2.2721620492879536e-07, learning_rate=0.005,\n",
       "               n_estimators=1300, num_leaves=800,\n",
       "               objective_type=&#x27;cross-entropy&#x27;, verbosity=-1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier(bagging_fraction=0.9938087600649544, bagging_freq=1,\n",
       "               class_weight='balanced', feature_fraction=0.4,\n",
       "               feature_pre_filter=False, lambda_l1=0.0007099817067951657,\n",
       "               lambda_l2=2.2721620492879536e-07, learning_rate=0.005,\n",
       "               n_estimators=1300, num_leaves=800,\n",
       "               objective_type='cross-entropy', verbosity=-1)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance_for_submission_lgb.fit(\n",
    "\t# train_data_stand_resampled,train_data_target_resampled\n",
    "\ttrain_data_standardized,\n",
    "\ttrain_data_target\n",
    "\t)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seeing important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_important=pd.DataFrame([train_data_encoded.columns,instance_for_submission_lgb.feature_importances_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DAYS_REGISTRATION</td>\n",
       "      <td>65077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SK_ID_CURR</td>\n",
       "      <td>63770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAYS_ID_PUBLISH</td>\n",
       "      <td>62880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>DAYS_LAST_PHONE_CHANGE</td>\n",
       "      <td>59209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DAYS_BIRTH</td>\n",
       "      <td>58951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMT_ANNUITY</td>\n",
       "      <td>57100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMT_CREDIT</td>\n",
       "      <td>53778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>EXT_SOURCE_2</td>\n",
       "      <td>50255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DAYS_EMPLOYED</td>\n",
       "      <td>50233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>EXT_SOURCE_3</td>\n",
       "      <td>46841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>REGION_POPULATION_RELATIVE</td>\n",
       "      <td>44244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMT_INCOME_TOTAL</td>\n",
       "      <td>40873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AMT_GOODS_PRICE</td>\n",
       "      <td>40414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HOUR_APPR_PROCESS_START</td>\n",
       "      <td>30420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_YEAR</td>\n",
       "      <td>21068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>OBS_30_CNT_SOCIAL_CIRCLE</td>\n",
       "      <td>17212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>OBS_60_CNT_SOCIAL_CIRCLE</td>\n",
       "      <td>12754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CNT_FAM_MEMBERS</td>\n",
       "      <td>10959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CNT_CHILDREN</td>\n",
       "      <td>7129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_QRT</td>\n",
       "      <td>6964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0      1\n",
       "9            DAYS_REGISTRATION  65077\n",
       "0                   SK_ID_CURR  63770\n",
       "10             DAYS_ID_PUBLISH  62880\n",
       "33      DAYS_LAST_PHONE_CHANGE  59209\n",
       "7                   DAYS_BIRTH  58951\n",
       "4                  AMT_ANNUITY  57100\n",
       "3                   AMT_CREDIT  53778\n",
       "27                EXT_SOURCE_2  50255\n",
       "8                DAYS_EMPLOYED  50233\n",
       "28                EXT_SOURCE_3  46841\n",
       "6   REGION_POPULATION_RELATIVE  44244\n",
       "2             AMT_INCOME_TOTAL  40873\n",
       "5              AMT_GOODS_PRICE  40414\n",
       "20     HOUR_APPR_PROCESS_START  30420\n",
       "59  AMT_REQ_CREDIT_BUREAU_YEAR  21068\n",
       "29    OBS_30_CNT_SOCIAL_CIRCLE  17212\n",
       "31    OBS_60_CNT_SOCIAL_CIRCLE  12754\n",
       "17             CNT_FAM_MEMBERS  10959\n",
       "1                 CNT_CHILDREN   7129\n",
       "58   AMT_REQ_CREDIT_BUREAU_QRT   6964"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_features_importance=features_important.T.sort_values(by=1,ascending=False)\n",
    "sorted_features_importance.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_train_data_pred=instance_for_submission_lgb.predict(train_data_standardized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for the train(total) data for lgb is: 0.9364166549230886\n",
      "f1 score for the train(total) data for lgb is: 0.8408228435960925\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy for the train(total) data for lgb is:\",accuracy_score(train_data_target,complete_train_data_pred))\n",
    "print(\"f1 score for the train(total) data for lgb is:\",f1_score(train_data_target,complete_train_data_pred,average=\"macro\"))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best submission "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "id": "yYkDF4zWBjTn"
   },
   "outputs": [],
   "source": [
    "predicted_target_for_test_data_in_lgbm=instance_for_submission_lgb.predict(test_data_standardized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yiX0-3TUBuke",
    "outputId": "99992793-f98c-4e9a-8a4d-d4493f068f7e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108937"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_of_0_in_lgbm=(predicted_target_for_test_data_in_lgbm==0).sum()\n",
    "count_of_0_in_lgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nczMTSgsB4Df",
    "outputId": "6f7654da-d56d-43ac-8ead-66967a5c658a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14068"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_of_1_in_lgbm=(predicted_target_for_test_data_in_lgbm==1).sum()\n",
    "count_of_1_in_lgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "id": "qQr1D-65B-AO"
   },
   "outputs": [],
   "source": [
    "submission[\"TARGET\"]=predicted_target_for_test_data_in_lgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "id": "rp14m4TACLl9"
   },
   "outputs": [],
   "source": [
    "submission.to_csv(\"./my_submission_lgbm_final.csv\",index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "instance_for_submission_lgb=LGBMClassifier(verbosity = -1,\n",
    "         feature_pre_filter = False,\n",
    "         objective_type=\"cross-entropy\",\n",
    "         lambda_l1= 0.0007099817067951657,\n",
    "         lambda_l2= 2.2721620492879536e-07,\n",
    "         num_leaves=800,\n",
    "         feature_fraction=0.4,\n",
    "         bagging_fraction= 0.9938087600649544,\n",
    "         bagging_freq=1,\n",
    "         min_child_samples=20,\n",
    "         learning_rate= 0.005,\n",
    "         n_estimators=1300,\n",
    "         class_weight= 'balanced'\n",
    "         )\n",
    "\n",
    "Accuracy for the train(total) data for lgb is: 0.9364166549230886\n",
    "\n",
    "f1 score for the train(total) data for lgb is: 0.8408228435960925 \n",
    "\n",
    "kaggle: 0.60775\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hit and try parameters tried without resampling and outlier removal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LGBMClassifier(verbosity = -1,\n",
    "         feature_pre_filter = False,\n",
    "         objective_type=\"cross-entropy\",\n",
    "         lambda_l1= 0.0007099817067951657,\n",
    "         lambda_l2= 2.2721620492879536e-07,\n",
    "         num_leaves=800,\n",
    "         feature_fraction=0.4,\n",
    "         bagging_fraction= 0.9938087600649544,\n",
    "         bagging_freq=1,\n",
    "         min_child_samples=30,\n",
    "         learning_rate= 0.005,\n",
    "         n_estimators=1300,\n",
    "         class_weight= 'balanced'\n",
    "         )\n",
    "\n",
    "Accuracy for the train(total) data for lgb is: 0.9341152345975035\n",
    "\n",
    "f1 score for the train(total) data for lgb is: 0.8365326318172227\n",
    "\n",
    "kaggle score: 0.60612"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LGBMClassifier(verbosity = -1,\n",
    "        #  feature_pre_filter = False,\n",
    "         objective_type=\"cross-entropy\",\n",
    "        #  num_iterations = 10000,\n",
    "         lambda_l1= 0.0007099817067951657,\n",
    "         lambda_l2= 2.2721620492879536e-07,\n",
    "         num_leaves=800,\n",
    "         feature_fraction=0.4,\n",
    "         bagging_fraction= 0.9938087600649544,\n",
    "         bagging_freq=1,\n",
    "         min_child_samples=50,\n",
    "         learning_rate= 0.005,\n",
    "         n_estimators=1500,\n",
    "         class_weight= 'balanced'\n",
    "         )\n",
    "\n",
    "\n",
    "Accuracy for the train(total) data for lgb is: 0.9427732902238835\n",
    "\n",
    "f1 score for the train(total) data for lgb is: 0.8531109845800675\n",
    "\n",
    "kaggle score: 0.60516"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Few results on hit and try parameters with resampling and outlier correction data."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "LGBMClassifier(\n",
    "       verbosity = -1,\n",
    "         feature_pre_filter = False,\n",
    "         objective_type=\"cross-entropy\",\n",
    "         feature_fraction=0.2,\n",
    "         bagging_fraction= 0.2,\n",
    "         bagging_freq=1,\n",
    "         min_child_samples=300,\n",
    "         class_weight= 'balanced'\n",
    "         )\n",
    "\n",
    "Accuracy for the train(total) data for lgb is: 0.8984739905716161\n",
    "\n",
    "f1 score for the train(total) data for lgb is: 0.6056715012686482\n",
    "\n",
    "kaggle score: 0.58979"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LGBMClassifier(\n",
    "       verbosity = -1,\n",
    "         feature_pre_filter = False,\n",
    "         objective_type=\"cross-entropy\",\n",
    "         feature_fraction=0.2,\n",
    "         bagging_fraction= 0.2,\n",
    "         bagging_freq=1,\n",
    "         n_estimators=1000,\n",
    "         min_child_samples=300,\n",
    "         class_weight= 'balanced'\n",
    "         )\n",
    "\n",
    "Accuracy for the train(total) data for lgb is: 0.910386954328214\n",
    "\n",
    "f1 score for the train(total) data for lgb is: 0.6828407863874724"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LGBMClassifier(\n",
    "       verbosity = -1,\n",
    "         feature_pre_filter = False,\n",
    "         objective_type=\"cross-entropy\",\n",
    "         feature_fraction=0.2,\n",
    "         bagging_fraction= 0.2,\n",
    "         bagging_freq=1,\n",
    "         n_estimators=2000,\n",
    "         min_child_samples=250,\n",
    "         class_weight= 'balanced'\n",
    "         )\n",
    "\n",
    "Accuracy for the train(total) data for lgb is: 0.9248550209670332\n",
    "\n",
    "f1 score for the train(total) data for lgb is: 0.7483159081222445\n",
    "\n",
    "kaggle score: 0.58211"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LGBMClassifier(\n",
    "        verbosity = -1,\n",
    "         feature_pre_filter = False,\n",
    "         objective_type=\"cross-entropy\",\n",
    "        # # #  num_iterations = 10000,\n",
    "        # #  lambda_l1= 0.0007099817067951657,\n",
    "        # #  lambda_l2= 2.2721620492879536e-07,\n",
    "        #  num_leaves=512,\n",
    "         feature_fraction=0.2,\n",
    "         bagging_fraction= 0.2,\n",
    "         n_estimators=1500,\n",
    "         class_weight= 'balanced'\n",
    "         )\n",
    "\t\t\t\t \n",
    "Accuracy for the train(total) data for lgb is: 0.9049621074445173\n",
    "\n",
    "f1 score for the train(total) data for lgb is: 0.6089854136365465\n",
    "\n",
    "kaggle score=0.5812"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "instance_for_submission_lgb=LGBMClassifier(\n",
    "       verbosity = -1,\n",
    "         feature_pre_filter = False,\n",
    "         objective_type=\"cross-entropy\",\n",
    "         feature_fraction=0.2,\n",
    "         bagging_fraction= 0.2,\n",
    "         n_estimators=1500,\n",
    "         min_child_samples=200,\n",
    "         class_weight= 'balanced'\n",
    "         )\n",
    "\n",
    "Accuracy for the train(total) data for lgb is: 0.9411404113118907\n",
    "\n",
    "f1 score for the train(total) data for lgb is: 0.7793209512201487\n",
    "\n",
    "kaggle score=0.57633"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LGBMClassifier(\n",
    "        verbosity = -1,\n",
    "         feature_pre_filter = False,\n",
    "         objective_type=\"cross-entropy\",\n",
    "         feature_fraction=0.2,\n",
    "         bagging_fraction= 0.2,\n",
    "         n_estimators=1000,\n",
    "         class_weight= 'balanced'\n",
    "         )\n",
    "\n",
    "Accuracy for the train(total) data for lgb is: 0.9305565350417985\n",
    "\n",
    "f1 score for the train(total) data for lgb is: 0.7326667787846357"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other models tried"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import LogisticRegression\n",
    "# from sklearn.metrics import accuracy_score,f1_score\n",
    "# instance_of_LogisticRegression=LogisticRegression(solver=\"lbfgs\",max_iter=10000,C=100)#,penalty=\"elasticnet\")\n",
    "# instance_of_LogisticRegression.fit(train_data_split,train_data_split_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_split_test_ypred=instance_of_LogisticRegression.predict(train_data_split_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 31,
     "status": "ok",
     "timestamp": 1668520405838,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "d6y_DFqNkxuU",
    "outputId": "499060f3-f42b-46a5-c40b-de447a2a1abc"
   },
   "outputs": [],
   "source": [
    "# print(\"Accuracy for the train(test) data for logistic regression is:\",accuracy_score(train_data_split_test_y,train_data_split_test_ypred))\n",
    "# print(\"f1 score for the train(test) data for logistic regression is:\",f1_score(train_data_split_test_y,train_data_split_test_ypred,average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_pred=instance_of_LogisticRegression.predict(train_data_standardized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"Accuracy for the train(total) data for logistic regression is:\",accuracy_score(train_data_target,train_data_pred))\n",
    "# print(\"f1 score for the train(total) data for logistic regression is:\",f1_score(train_data_target,train_data_pred,average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1668520405838,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "I4Wa7iIzlyzx"
   },
   "outputs": [],
   "source": [
    "# predicted_target_for_test_data_in_logistic_regression=instance_of_LogisticRegression.predict(test_data_standardized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1668520405838,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "mqUP637olhDT",
    "outputId": "dfdb6b3f-836d-4a47-9730-9633c1935620"
   },
   "outputs": [],
   "source": [
    "# count_of_0_in_logistic_regression=(predicted_target_for_test_data_in_logistic_regression==0).sum()\n",
    "# count_of_0_in_logistic_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1668520405839,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "O5uK8Ibnl4OE",
    "outputId": "3f4e696a-9de7-46c2-92a7-3a34f53c5c1d"
   },
   "outputs": [],
   "source": [
    "# count_of_1_in_logistic_regression=(predicted_target_for_test_data_in_logistic_regression==1).sum()\n",
    "# count_of_1_in_logistic_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1668520405839,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "i_di2JDqoRyE"
   },
   "outputs": [],
   "source": [
    "# submission[\"TARGET\"]=predicted_target_for_test_data_in_logistic_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "executionInfo": {
     "elapsed": 1558,
     "status": "ok",
     "timestamp": 1668520407390,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "sy6wJt82oZpw"
   },
   "outputs": [],
   "source": [
    "# submission.to_csv(\"./my_submission_logistic_regression_sampled.csv\",index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.naive_bayes import GaussianNB\n",
    "# instance_of_gnb=GaussianNB()\n",
    "# instance_of_gnb.fit(train_data_standardized,train_data_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data_split_test_ypred=instance_of_gnb.predict(train_data_split_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 34,
     "status": "ok",
     "timestamp": 1668520408186,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "SkkvdQcKlB2Z",
    "outputId": "fd899cb4-c906-4ec2-bbfd-159f304b63e0"
   },
   "outputs": [],
   "source": [
    "# print(\"Accuracy for the train data for naive bayes is:\",accuracy_score(train_data_split_test_y,train_data_split_test_ypred))\n",
    "# print(\"f1 score for the train data for naive bayes is:\",f1_score(train_data_split_test_y,train_data_split_test_ypred,average=\"micro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "executionInfo": {
     "elapsed": 29,
     "status": "ok",
     "timestamp": 1668520408187,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "rRU-fe0NmHyE"
   },
   "outputs": [],
   "source": [
    "# predicted_target_for_test_data_in_naive_bayes=instance_of_gnb.predict(test_data_standardized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 28,
     "status": "ok",
     "timestamp": 1668520408187,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "3FiAOnCsmVwC",
    "outputId": "c4f58d78-e0ad-4bf2-e87e-054e8c313b09"
   },
   "outputs": [],
   "source": [
    "# count_of_0_in_naive_bayes=(predicted_target_for_test_data_in_naive_bayes==0).sum()\n",
    "# count_of_0_in_naive_bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1668520408188,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "Yv_BxWIkmdJ5",
    "outputId": "b5e693ce-d232-4b03-f2c6-5face78d3b2a"
   },
   "outputs": [],
   "source": [
    "# count_of_1_in_naive_bayes=(predicted_target_for_test_data_in_naive_bayes==1).sum()\n",
    "# count_of_1_in_naive_bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1668520408188,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "bUf1c71zohVA"
   },
   "outputs": [],
   "source": [
    "# submission[\"TARGET\"]=predicted_target_for_test_data_in_naive_bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1668520408188,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "7Hsvw1pTokAG"
   },
   "outputs": [],
   "source": [
    "# submission.to_csv(\"./my_submission_naive_bayes.csv\",index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "x3wlheFRpUA0"
   },
   "source": [
    "## Decision tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17073,
     "status": "ok",
     "timestamp": 1668520425253,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "VP5-crx6pcTF",
    "outputId": "3a43af95-f53e-40e3-b54c-63c781fb428d"
   },
   "outputs": [],
   "source": [
    "# from sklearn.tree import DecisionTreeClassifier\n",
    "# instance_of_tree_classifier=DecisionTreeClassifier(random_state=42,max_features=\"auto\",max_depth=24,criterion=\"entropy\")\n",
    "# instance_of_tree_classifier.fit(train_data_split,train_data_split_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "executionInfo": {
     "elapsed": 44,
     "status": "ok",
     "timestamp": 1668520425253,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "ZSaQOILjqYNh"
   },
   "outputs": [],
   "source": [
    "# predicted_target_for_train_data_in_decision_trees=instance_of_tree_classifier.predict(train_data_split_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 41,
     "status": "ok",
     "timestamp": 1668520425254,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "asF17MArqoVo",
    "outputId": "4f48d217-4510-4bc2-b23e-dbf9caf2808d"
   },
   "outputs": [],
   "source": [
    "# print(\"Accuracy for the train data for deision trees is:\",accuracy_score(train_data_split_test_y,predicted_target_for_train_data_in_decision_trees))\n",
    "# print(\"f1 score for the train data for decision trees is:\",f1_score(train_data_split_test_y,predicted_target_for_train_data_in_decision_trees,average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trainpredy=instance_of_tree_classifier.predict(train_data_standardized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"Accuracy for the train data for deision trees is:\",accuracy_score(train_data_target,trainpredy))\n",
    "# print(\"f1 score for the train data for decision trees is:\",f1_score(train_data_target,trainpredy,average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "executionInfo": {
     "elapsed": 940,
     "status": "ok",
     "timestamp": 1668520426168,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "o04UrxtgrNCr"
   },
   "outputs": [],
   "source": [
    "# predicted_target_for_test_data_in_decision_trees=instance_of_tree_classifier.predict(test_data_standardized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 41,
     "status": "ok",
     "timestamp": 1668520426168,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "kdjg7I_Fraxx",
    "outputId": "e4c6bbf9-14c0-4d21-8785-05ee95aaf350"
   },
   "outputs": [],
   "source": [
    "# count_of_0_in_decision_trees=(predicted_target_for_test_data_in_decision_trees==0).sum()\n",
    "# count_of_0_in_decision_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 36,
     "status": "ok",
     "timestamp": 1668520426169,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "XFeg8Jlgrlgg",
    "outputId": "deb25935-4025-4966-855b-f965d8115f0a"
   },
   "outputs": [],
   "source": [
    "# count_of_1_in_decision_trees=(predicted_target_for_test_data_in_decision_trees==1).sum()\n",
    "# count_of_1_in_decision_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1668520426169,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "GCQTpRSFrprw"
   },
   "outputs": [],
   "source": [
    "# submission[\"TARGET\"]=predicted_target_for_test_data_in_decision_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1668520426170,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "1y19oLLLrt1M"
   },
   "outputs": [],
   "source": [
    "# submission.to_csv(\"./my_submission_decision_trees_resampled2.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (pd.read_csv(\"./my_submission_decision_trees_resampled1.csv\")[\"TARGET\"]==1).sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# svm_clf=SVC(C=0.005,kernel=\"rbf\",gamma=\"scale\",random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "# svm_clf.fit(train_data_split,train_data_split_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicted_target_for_train_data_in_svm=svm_clf.predict(train_data_split_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.metrics import accuracy_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 41,
     "status": "ok",
     "timestamp": 1668520425254,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "asF17MArqoVo",
    "outputId": "4f48d217-4510-4bc2-b23e-dbf9caf2808d"
   },
   "outputs": [],
   "source": [
    "# print(\"Accuracy for the train data for svm is:\",accuracy_score(train_data_target,predicted_target_for_train_data_in_svm))\n",
    "# print(\"f1 score for the train data for svm is:\",f1_score(train_data_target,predicted_target_for_train_data_in_svm,average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "executionInfo": {
     "elapsed": 940,
     "status": "ok",
     "timestamp": 1668520426168,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "o04UrxtgrNCr"
   },
   "outputs": [],
   "source": [
    "# predicted_target_for_test_data_in_svm=svm_clf.predict(test_data_standardized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 41,
     "status": "ok",
     "timestamp": 1668520426168,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "kdjg7I_Fraxx",
    "outputId": "e4c6bbf9-14c0-4d21-8785-05ee95aaf350"
   },
   "outputs": [],
   "source": [
    "# count_of_0_in_svm=(predicted_target_for_test_data_in_svm==0).sum()\n",
    "# count_of_0_in_svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 36,
     "status": "ok",
     "timestamp": 1668520426169,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "XFeg8Jlgrlgg",
    "outputId": "deb25935-4025-4966-855b-f965d8115f0a"
   },
   "outputs": [],
   "source": [
    "# count_of_1_in_svm=(predicted_target_for_test_data_in_svm==1).sum()\n",
    "# count_of_1_in_svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1668520426169,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "GCQTpRSFrprw"
   },
   "outputs": [],
   "source": [
    "# submission[\"TARGET\"]=predicted_target_for_test_data_in_svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1668520426170,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "1y19oLLLrt1M"
   },
   "outputs": [],
   "source": [
    "# submission.to_csv(\"./my_submission_svm.csv\",index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "axX5tDqxRHuH"
   },
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "executionInfo": {
     "elapsed": 544,
     "status": "ok",
     "timestamp": 1668521214868,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "Ujyn6rWIRLJL"
   },
   "outputs": [],
   "source": [
    "# params={\n",
    "#     \"learning_rate\":[0.01,0.02,0.03,0.04,0.05,0.10], \n",
    "#     \"max_depth\":[7,8,9,10,11,12,15],\n",
    "#     \"min_child_weight\":[1,2,3],\n",
    "#     \"gamma\":[0.7,0.9,0.93,0.95,0.97,0.99],\n",
    "#     \"colsample_bytree\":[0.3,0.4,0.5,0.6,0.7]\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "executionInfo": {
     "elapsed": 567,
     "status": "ok",
     "timestamp": 1668521221812,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "qomL7D7sSjYy"
   },
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import RandomizedSearchCV,GridSearchCV\n",
    "# import xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1668521224179,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "pnLRCxiaTSls"
   },
   "outputs": [],
   "source": [
    "# classifier=xgboost.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "executionInfo": {
     "elapsed": 615,
     "status": "ok",
     "timestamp": 1668521229988,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "tMp5QHONTZm4"
   },
   "outputs": [],
   "source": [
    "# random_search=RandomizedSearchCV(classifier,param_distributions=params,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 522417,
     "status": "ok",
     "timestamp": 1668521755171,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "USDDwuHjULkI",
    "outputId": "09f6aaf9-02e2-4413-dc24-517f4c7267f1"
   },
   "outputs": [],
   "source": [
    "# random_search.fit(train_data_standardized,train_data_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 595,
     "status": "ok",
     "timestamp": 1668521761978,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "qCkE54zteRbt",
    "outputId": "9f8fab8b-51b1-4c42-caa9-0c868f908f89"
   },
   "outputs": [],
   "source": [
    "# random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 601,
     "status": "ok",
     "timestamp": 1668521784091,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "yH_S-8Z5edvq",
    "outputId": "203a1bae-0f4a-4ee2-851a-52b781bd2f97"
   },
   "outputs": [],
   "source": [
    "# random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1668521810418,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "54nm6RGqe4Ai"
   },
   "outputs": [],
   "source": [
    "# classifier=xgboost.XGBClassifier(learning_rate =0.02,\n",
    "# n_estimators=5000,\n",
    "# min_child_weight=1,\n",
    "# objective= 'binary:logistic', \n",
    "# nthread=8,\n",
    "# eval_metric=\"logloss\",\n",
    "# scale_pos_weight=1, \n",
    "# seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 105358,
     "status": "ok",
     "timestamp": 1668521919482,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "BUIV41s2f52X",
    "outputId": "0c1940a1-a537-4e31-a064-2c3a7f4328f9"
   },
   "outputs": [],
   "source": [
    "# classifier.fit(train_data_split,train_data_split_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "classifier=xgboost.XGBClassifier(learning_rate =0.02,\n",
    "n_estimators=10000,\n",
    "objective= 'binary:logistic', \n",
    "nthread=8,\n",
    "eval_metric=\"logloss\",\n",
    "scale_pos_weight=2, \n",
    "seed=42)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "              colsample_bynode=1, colsample_bytree=0.8,\n",
    "              enable_categorical=False, gamma=0, gpu_id=-1,\n",
    "              importance_type=None, interaction_constraints='',\n",
    "              learning_rate=0.12, max_delta_step=0, max_depth=6,\n",
    "              min_child_weight=2, missing=nan, monotone_constraints='()',\n",
    "              n_estimators=5000, n_jobs=8, nthread=8, num_parallel_tree=1,\n",
    "              predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1,\n",
    "              scale_pos_weight=1.5, seed=42, subsample=0.9192709180189262,\n",
    "              tree_method='exact', validate_parameters=1, verbosity=None)\n",
    "\n",
    "Accuracy for the train data for xgb is: 0.9999186983056727 \n",
    "\n",
    "f1 score for the train data for xgb is: 0.9997260013509839\n",
    "\n",
    "kaggle: 0.52381"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "executionInfo": {
     "elapsed": 2788,
     "status": "ok",
     "timestamp": 1668521926263,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "QgSYl-rxgz5S"
   },
   "outputs": [],
   "source": [
    "# predicted_target_for_train_data_in_xgb=classifier.predict(train_data_split_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 604,
     "status": "ok",
     "timestamp": 1668521930335,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "fQTmMXXEimoq",
    "outputId": "53270b0e-1dd3-445d-d6e7-df29b6090839"
   },
   "outputs": [],
   "source": [
    "# print(\"Accuracy for the train data for xgb is:\",accuracy_score(train_data_split_test_y,predicted_target_for_train_data_in_xgb))\n",
    "# print(\"f1 score for the train data for xgb is:\",f1_score(train_data_split_test_y,predicted_target_for_train_data_in_xgb,average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trainpred=classifier.predict(train_data_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"Accuracy for the train data for xgb is:\",accuracy_score(train_data_split_y,trainpred))\n",
    "# print(\"f1 score for the train data for xgb is:\",f1_score(train_data_split_y,trainpred,average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "executionInfo": {
     "elapsed": 1789,
     "status": "ok",
     "timestamp": 1668521935793,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "ZsA3T-apiNyn"
   },
   "outputs": [],
   "source": [
    "# predicted_target_for_test_data_in_xgb=classifier.predict(test_data_standardized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1091,
     "status": "ok",
     "timestamp": 1668521943486,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "HytnRgGbhdlW",
    "outputId": "b90b1f60-ec40-416e-dae4-33825d5df08a"
   },
   "outputs": [],
   "source": [
    "# count_of_0_in_xgb=(predicted_target_for_test_data_in_xgb==0).sum()\n",
    "# count_of_0_in_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1143,
     "status": "ok",
     "timestamp": 1668521947343,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "FtBZkKjFiSr6",
    "outputId": "16e28b2b-17db-492f-8710-e98489c44125"
   },
   "outputs": [],
   "source": [
    "# count_of_1_in_xgb=(predicted_target_for_test_data_in_xgb==1).sum()\n",
    "# count_of_1_in_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1668521951327,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "DbRyVtEIita7"
   },
   "outputs": [],
   "source": [
    "# submission[\"TARGET\"]=predicted_target_for_test_data_in_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "# submission.to_csv(\"./my_submission_xgb_resampled.csv\",index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "# n_estimators = [int(x) for x in np.linspace(start=100, stop=1000, num=10)]\n",
    "# max_features = ['auto', 'sqrt']\n",
    "# max_depth = [int(x) for x in np.linspace(10, 110, num=11)]\n",
    "# max_depth.append(None)\n",
    "# min_samples_split = [2, 5, 10]\n",
    "# min_samples_leaf = [1, 2, 4]\n",
    "# bootstrap = [True, False]\n",
    "\n",
    "# random_grid = {'n_estimators': n_estimators, 'max_features': max_features,\n",
    "#                'max_depth': max_depth, 'min_samples_split': min_samples_split,\n",
    "#                'min_samples_leaf': min_samples_leaf, 'bootstrap': bootstrap}\n",
    "\n",
    "# random_forest_clf = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# random_forest_clf_cv = RandomizedSearchCV(estimator=random_forest_clf, scoring='f1_macro',param_distributions=random_grid, n_iter=100, cv=3, verbose=2, random_state=42, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random_forest_clf_cv.fit(train_data_standardized,train_data_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random_forest_clf_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random_forest_clf_best=RandomForestClassifier( max_depth=16,max_features='sqrt', n_estimators=2000,random_state=42,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random_forest_clf_best.fit(train_data_standardized,train_data_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicted_target_for_train_data_in_random_forest_best=random_forest_clf_best.predict(train_data_split_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"Accuracy for the train(test) data for rfb is:\",accuracy_score(train_data_split_test_y,predicted_target_for_train_data_in_random_forest_best))\n",
    "# print(\"f1 score for the train(test) data for rfb is:\",f1_score(train_data_split_test_y,predicted_target_for_train_data_in_random_forest_best,average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trainpred=random_forest_clf_best.predict(train_data_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"Accuracy for the train data for rfb is:\",accuracy_score(train_data_split_y,trainpred))\n",
    "# print(\"f1 score for the train data for rfb is:\",f1_score(train_data_split_y,trainpred,average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicted_target_for_train_data_full_in_random_forest_best=random_forest_clf_best.predict(train_data_standardized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"Accuracy for the train data for rfb is:\",accuracy_score(train_data_target,predicted_target_for_train_data_full_in_random_forest_best))\n",
    "# print(\"f1 score for the train data for rfb is:\",f1_score(train_data_target,predicted_target_for_train_data_full_in_random_forest_best,average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "executionInfo": {
     "elapsed": 1789,
     "status": "ok",
     "timestamp": 1668521935793,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "ZsA3T-apiNyn"
   },
   "outputs": [],
   "source": [
    "# predicted_target_for_test_data_in_random_forest_best=random_forest_clf_best.predict(test_data_standardized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1091,
     "status": "ok",
     "timestamp": 1668521943486,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "HytnRgGbhdlW",
    "outputId": "b90b1f60-ec40-416e-dae4-33825d5df08a"
   },
   "outputs": [],
   "source": [
    "# count_of_0_in_xgb=(predicted_target_for_test_data_in_random_forest_best==0).sum()\n",
    "# count_of_0_in_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1143,
     "status": "ok",
     "timestamp": 1668521947343,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "FtBZkKjFiSr6",
    "outputId": "16e28b2b-17db-492f-8710-e98489c44125"
   },
   "outputs": [],
   "source": [
    "# count_of_1_in_xgb=(predicted_target_for_test_data_in_random_forest_best==1).sum()\n",
    "# count_of_1_in_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1668521951327,
     "user": {
      "displayName": "Shashank Shekhar",
      "userId": "16017809737637556764"
     },
     "user_tz": -330
    },
    "id": "DbRyVtEIita7"
   },
   "outputs": [],
   "source": [
    "# submission[\"TARGET\"]=predicted_target_for_test_data_in_random_forest_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "# submission.to_csv(\"./my_submission_random_forest_best_clf.csv\",index=False)"
   ]
  }
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