Zimnat Insurance Recommendation Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SHOLAY\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "#import necessary dependecies\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np  \n",
    "import pandas as pd, os, gc\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc, log_loss\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "%matplotlib inline\n",
    "# from typing import List"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Environment setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 50)\n",
    "pd.set_option('display.max_rows', 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "trains = 'Train.csv'\n",
    "tests = 'Test.csv'\n",
    "submissions = 'SampleSubmission.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function to load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(data, parse_date=None):\n",
    "    if parse_date is not None:\n",
    "        return pd.read_csv(data, parse_dates=[parse_date])\n",
    "    else:\n",
    "        return pd.read_csv(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = read_data(trains, parse_date='join_date')\n",
    "test = read_data(tests, parse_date='join_date')\n",
    "submission= read_data(submissions)\n",
    "del trains, tests, submissions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>birth_year</th>\n",
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       "      <td>2019-01-02</td>\n",
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       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>2013-01-06</td>\n",
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       "      <td>1991</td>\n",
       "      <td>748L</td>\n",
       "      <td>QZYX</td>\n",
       "      <td>90QI</td>\n",
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      "text/plain": [
       "        ID  join_date sex marital_status  birth_year branch_code  \\\n",
       "0  4WKQSBB 2019-01-02   F              M        1987        1X1H   \n",
       "1  CP5S02H 2019-01-06   F              M        1981        UAOD   \n",
       "2  2YKDILJ 2013-01-06   M              U        1991        748L   \n",
       "3  2S9E81J 2019-01-08   M              M        1990        1X1H   \n",
       "4  BHDYVFT 2019-01-08   M              M        1990        748L   \n",
       "\n",
       "  occupation_code occupation_category_code  P5DA  RIBP  8NN1  7POT  66FJ  \\\n",
       "0            2A7I                     T4MS     0     0     0     0     0   \n",
       "1            2A7I                     T4MS     0     0     0     0     0   \n",
       "2            QZYX                     90QI     0     0     0     0     0   \n",
       "3            BP09                     56SI     0     0     0     0     0   \n",
       "4            NO3L                     T4MS     0     0     0     0     0   \n",
       "\n",
       "   GYSR  SOP4  RVSZ  PYUQ  LJR9  N2MW  AHXO  BSTQ  FM3X  K6QO  QBOL  JWFN  \\\n",
       "0     0     0     1     0     0     0     0     0     0     1     0     0   \n",
       "1     0     0     1     0     0     0     0     0     0     1     0     0   \n",
       "2     0     1     1     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     1     0     0     0     0     0     0     1     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   JZ9D  J9JW  GHYX  ECY3  \n",
       "0     0     0     0     0  \n",
       "1     0     0     0     0  \n",
       "2     0     0     0     1  \n",
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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       "      <td>2020-01-02</td>\n",
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      "text/plain": [
       "        ID  join_date sex marital_status  birth_year branch_code  \\\n",
       "0  F86J5PC 2018-01-12   M              M        1984        94KC   \n",
       "1  H6141K3 2019-01-10   M              M        1996        1X1H   \n",
       "2  RBAYUXZ 2020-01-01   F              W        1968        UAOD   \n",
       "3  KCBILBQ 2019-01-02   M              M        1989        94KC   \n",
       "4  LSEC1ZJ 2020-01-02   F              M        1982        UAOD   \n",
       "\n",
       "  occupation_code occupation_category_code  P5DA  RIBP  8NN1  7POT  66FJ  \\\n",
       "0            DZRV                     90QI     0     0     0     0     0   \n",
       "1            J9SY                     90QI     0     0     0     0     0   \n",
       "2            2A7I                     T4MS     0     0     0     0     0   \n",
       "3            2A7I                     T4MS     0     0     0     0     0   \n",
       "4            0KID                     T4MS     0     0     0     0     0   \n",
       "\n",
       "   GYSR  SOP4  RVSZ  PYUQ  LJR9  N2MW  AHXO  BSTQ  FM3X  K6QO  QBOL  JWFN  \\\n",
       "0     0     0     1     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     1     0     0     0     0     0     0     1     0     0   \n",
       "2     0     0     1     0     0     0     0     0     0     1     0     0   \n",
       "3     0     0     1     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   JZ9D  J9JW  GHYX  ECY3  \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     1     0     0     0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 29132 entries, 0 to 29131\n",
      "Data columns (total 29 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   ID                        29132 non-null  object        \n",
      " 1   join_date                 29130 non-null  datetime64[ns]\n",
      " 2   sex                       29132 non-null  object        \n",
      " 3   marital_status            29132 non-null  object        \n",
      " 4   birth_year                29132 non-null  int64         \n",
      " 5   branch_code               29132 non-null  object        \n",
      " 6   occupation_code           29132 non-null  object        \n",
      " 7   occupation_category_code  29132 non-null  object        \n",
      " 8   P5DA                      29132 non-null  int64         \n",
      " 9   RIBP                      29132 non-null  int64         \n",
      " 10  8NN1                      29132 non-null  int64         \n",
      " 11  7POT                      29132 non-null  int64         \n",
      " 12  66FJ                      29132 non-null  int64         \n",
      " 13  GYSR                      29132 non-null  int64         \n",
      " 14  SOP4                      29132 non-null  int64         \n",
      " 15  RVSZ                      29132 non-null  int64         \n",
      " 16  PYUQ                      29132 non-null  int64         \n",
      " 17  LJR9                      29132 non-null  int64         \n",
      " 18  N2MW                      29132 non-null  int64         \n",
      " 19  AHXO                      29132 non-null  int64         \n",
      " 20  BSTQ                      29132 non-null  int64         \n",
      " 21  FM3X                      29132 non-null  int64         \n",
      " 22  K6QO                      29132 non-null  int64         \n",
      " 23  QBOL                      29132 non-null  int64         \n",
      " 24  JWFN                      29132 non-null  int64         \n",
      " 25  JZ9D                      29132 non-null  int64         \n",
      " 26  J9JW                      29132 non-null  int64         \n",
      " 27  GHYX                      29132 non-null  int64         \n",
      " 28  ECY3                      29132 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(22), object(6)\n",
      "memory usage: 6.4+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total columns for training set: 29\n",
      "Total columns for testing set: 29\n"
     ]
    }
   ],
   "source": [
    "print(f'Total columns for training set: {len(train.columns)}');print(f'Total columns for testing set: {len(train.columns)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns == test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape: (29132, 29)\n",
      "Test shape:  (10000, 29)\n",
      "Submission shape: (210000, 2)\n"
     ]
    }
   ],
   "source": [
    "#print all dataset shape\n",
    "print(f'Train shape: {train.shape}\\nTest shape:  {test.shape}\\nSubmission shape: {submission.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Store Products ID in Multidimensional array\n",
    "col= ['P5DA', 'RIBP', '8NN1',\n",
    "       '7POT', '66FJ', 'GYSR', 'SOP4', 'RVSZ', 'PYUQ', 'LJR9', 'N2MW', 'AHXO',\n",
    "       'BSTQ', 'FM3X', 'K6QO', 'QBOL', 'JWFN', 'JZ9D', 'J9JW', 'GHYX', 'ECY3']\n",
    "products = train[col]\n",
    "products_t = test[col]\n",
    "del col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Axis Transformation\n",
    "train = train.melt(id_vars=train.columns[:8], value_vars=products, var_name = \"PCODE\", value_name=\"Label\" )\n",
    "test = test.melt(id_vars=test.columns[:8], value_vars=products, var_name = \"PCODE\", value_name=\"Label\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#A merger\n",
    "train['combiner']='x'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set\n",
    "data=pd.concat([train,test],sort=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>ID</th>\n",
       "      <th>join_date</th>\n",
       "      <th>sex</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>occupation_code</th>\n",
       "      <th>occupation_category_code</th>\n",
       "      <th>PCODE</th>\n",
       "      <th>Label</th>\n",
       "      <th>combiner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>331527</th>\n",
       "      <td>URKZC58</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1993</td>\n",
       "      <td>E5SW</td>\n",
       "      <td>0B60</td>\n",
       "      <td>JD7X</td>\n",
       "      <td>AHXO</td>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221475</th>\n",
       "      <td>RUDLK09</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>1987</td>\n",
       "      <td>E5SW</td>\n",
       "      <td>0KID</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>RVSZ</td>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166960</th>\n",
       "      <td>0SF5XBC</td>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1968</td>\n",
       "      <td>748L</td>\n",
       "      <td>2A7I</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>GYSR</td>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  join_date sex marital_status  birth_year branch_code  \\\n",
       "331527  URKZC58 2019-01-04   M              M        1993        E5SW   \n",
       "221475  RUDLK09 2020-01-05   F              M        1987        E5SW   \n",
       "166960  0SF5XBC 2017-01-07   M              M        1968        748L   \n",
       "\n",
       "       occupation_code occupation_category_code PCODE  Label combiner  \n",
       "331527            0B60                     JD7X  AHXO      0        x  \n",
       "221475            0KID                     T4MS  RVSZ      1        x  \n",
       "166960            2A7I                     T4MS  GYSR      0        x  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preview random 3 sample\n",
    "data.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================\n",
      "TRAIN LABEL DISTRIBUTION\n",
      "====================================\n",
      "0    545419\n",
      "1     66353\n",
      "Name: Label, dtype: int64\n",
      "====================================\n",
      "TEST LABEL DISTRIBUTION\n",
      "====================================\n",
      "0    197147\n",
      "1     12853\n",
      "Name: Label, dtype: int64\n",
      "====================================\n",
      "SAMPLE SUBMISSION LABEL DISTRIBUTION\n",
      "====================================\n",
      "0    197147\n",
      "1     12853\n",
      "Name: Label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Confirm Distribution (TEST AND SAMPLE SUBMISSION FILE)\n",
    "print('==' * 18);print('TRAIN LABEL DISTRIBUTION');print('==' * 18);print(train['Label'].value_counts())\n",
    "print('==' * 18);print('TEST LABEL DISTRIBUTION');print('==' * 18);print(test['Label'].value_counts())\n",
    "print('==' * 18);print('SAMPLE SUBMISSION LABEL DISTRIBUTION');print('==' * 18);print(submission['Label'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(611772, 10) (210000, 9) (210000, 2)\n"
     ]
    }
   ],
   "source": [
    "train=data[data.combiner.notnull()].reset_index(drop=True)\n",
    "test=data[data.combiner.isna()].reset_index(drop=True)\n",
    "train.drop('combiner', inplace=True, axis=1)\n",
    "test.drop(['Label','combiner'], inplace=True, axis=1)\n",
    "print(train.shape, test.shape, submission.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Concatenate new column towards submission requirement\n",
    "train['ID X PCODE'] = train['ID'] + ' X ' + train['PCODE']\n",
    "test['ID X PCODE'] = test['ID'] + ' X ' + test['PCODE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Re-arrange column (not necessary)\n",
    "col = ['ID', 'join_date', 'sex', 'marital_status', 'birth_year', 'branch_code',\\\n",
    "       'occupation_code', 'occupation_category_code', 'PCODE','ID X PCODE']\n",
    "test = test[col]\n",
    "col.append('Label')\n",
    "train = train[col]\n",
    "del col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>join_date</th>\n",
       "      <th>sex</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>occupation_code</th>\n",
       "      <th>occupation_category_code</th>\n",
       "      <th>PCODE</th>\n",
       "      <th>ID X PCODE</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4WKQSBB</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>1987</td>\n",
       "      <td>1X1H</td>\n",
       "      <td>2A7I</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>P5DA</td>\n",
       "      <td>4WKQSBB X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CP5S02H</td>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>1981</td>\n",
       "      <td>UAOD</td>\n",
       "      <td>2A7I</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>P5DA</td>\n",
       "      <td>CP5S02H X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2YKDILJ</td>\n",
       "      <td>2013-01-06</td>\n",
       "      <td>M</td>\n",
       "      <td>U</td>\n",
       "      <td>1991</td>\n",
       "      <td>748L</td>\n",
       "      <td>QZYX</td>\n",
       "      <td>90QI</td>\n",
       "      <td>P5DA</td>\n",
       "      <td>2YKDILJ X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2S9E81J</td>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1990</td>\n",
       "      <td>1X1H</td>\n",
       "      <td>BP09</td>\n",
       "      <td>56SI</td>\n",
       "      <td>P5DA</td>\n",
       "      <td>2S9E81J X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BHDYVFT</td>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1990</td>\n",
       "      <td>748L</td>\n",
       "      <td>NO3L</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>P5DA</td>\n",
       "      <td>BHDYVFT X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  join_date sex marital_status  birth_year branch_code  \\\n",
       "0  4WKQSBB 2019-01-02   F              M        1987        1X1H   \n",
       "1  CP5S02H 2019-01-06   F              M        1981        UAOD   \n",
       "2  2YKDILJ 2013-01-06   M              U        1991        748L   \n",
       "3  2S9E81J 2019-01-08   M              M        1990        1X1H   \n",
       "4  BHDYVFT 2019-01-08   M              M        1990        748L   \n",
       "\n",
       "  occupation_code occupation_category_code PCODE      ID X PCODE  Label  \n",
       "0            2A7I                     T4MS  P5DA  4WKQSBB X P5DA      0  \n",
       "1            2A7I                     T4MS  P5DA  CP5S02H X P5DA      0  \n",
       "2            QZYX                     90QI  P5DA  2YKDILJ X P5DA      0  \n",
       "3            BP09                     56SI  P5DA  2S9E81J X P5DA      0  \n",
       "4            NO3L                     T4MS  P5DA  BHDYVFT X P5DA      0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>join_date</th>\n",
       "      <th>sex</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>occupation_code</th>\n",
       "      <th>occupation_category_code</th>\n",
       "      <th>PCODE</th>\n",
       "      <th>ID X PCODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F86J5PC</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1984</td>\n",
       "      <td>94KC</td>\n",
       "      <td>DZRV</td>\n",
       "      <td>90QI</td>\n",
       "      <td>P5DA</td>\n",
       "      <td>F86J5PC X P5DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H6141K3</td>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1996</td>\n",
       "      <td>1X1H</td>\n",
       "      <td>J9SY</td>\n",
       "      <td>90QI</td>\n",
       "      <td>P5DA</td>\n",
       "      <td>H6141K3 X P5DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RBAYUXZ</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>F</td>\n",
       "      <td>W</td>\n",
       "      <td>1968</td>\n",
       "      <td>UAOD</td>\n",
       "      <td>2A7I</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>P5DA</td>\n",
       "      <td>RBAYUXZ X P5DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KCBILBQ</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1989</td>\n",
       "      <td>94KC</td>\n",
       "      <td>2A7I</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>P5DA</td>\n",
       "      <td>KCBILBQ X P5DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSEC1ZJ</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>1982</td>\n",
       "      <td>UAOD</td>\n",
       "      <td>0KID</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>P5DA</td>\n",
       "      <td>LSEC1ZJ X P5DA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  join_date sex marital_status  birth_year branch_code  \\\n",
       "0  F86J5PC 2018-01-12   M              M        1984        94KC   \n",
       "1  H6141K3 2019-01-10   M              M        1996        1X1H   \n",
       "2  RBAYUXZ 2020-01-01   F              W        1968        UAOD   \n",
       "3  KCBILBQ 2019-01-02   M              M        1989        94KC   \n",
       "4  LSEC1ZJ 2020-01-02   F              M        1982        UAOD   \n",
       "\n",
       "  occupation_code occupation_category_code PCODE      ID X PCODE  \n",
       "0            DZRV                     90QI  P5DA  F86J5PC X P5DA  \n",
       "1            J9SY                     90QI  P5DA  H6141K3 X P5DA  \n",
       "2            2A7I                     T4MS  P5DA  RBAYUXZ X P5DA  \n",
       "3            2A7I                     T4MS  P5DA  KCBILBQ X P5DA  \n",
       "4            0KID                     T4MS  P5DA  LSEC1ZJ X P5DA  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not in Test PCODE  []\n",
      "Not in Train PCODE  []\n",
      "============================================================\n",
      "Not in Test occupation_code  ['IE90', 'Q0LY', 'JSAX', '6XXU', '8HRZ', 'OQMY', 'INEJ', 'VZN9', 'UC7E', 'PSUY', 'WSRG', 'JQH3', 'LGTN', '738L', 'QQUP', '2XZ1', 'CAAV', 'LLLH', 'W1X2', 'DHSN', 'IX8T', '2US6', 'ZWPL', 'MEFQ', '9B5B', 'JUIP', 'BFD1', 'A4ZC', 'IMHI', 'E5PF', 'GZA8', '3YQ1', 'PJR4', 'NDL9', 'PPNK', '2686', '5LNN', '374O', 'URYD', 'M0WG', 'KBWO', 'ONY7', 'VYSA', 'KUPK', 'R7GL', 'HSVE', 'BER4', '6SKY', 'RH2K', 'ZHC2', 'W3ZV', 'FLXH', 'UYDZ', 'YJXM', '59QM']\n",
      "Not in Train occupation_code  ['0ZND', '8CHJ', '9F96', 'HSI5', '93OJ', 'BIA0', 'E2MJ', 'JBJP', '0FOI']\n",
      "============================================================\n",
      "Not in Test branch_code  []\n",
      "Not in Train branch_code  []\n",
      "============================================================\n",
      "Not in Test occupation_category_code  []\n",
      "Not in Train occupation_category_code  []\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "#Lets Check for data co-existence in Train/Test\n",
    "for col in ['PCODE','occupation_code', 'branch_code','occupation_category_code']:\n",
    "    train_col = train[col].unique();test_col = test[col].unique()\n",
    "    print ('Not in Test ' + col + ' ',[i  for i in train_col if i not in test_col])\n",
    "    print ('Not in Train ' + col + ' ',[i  for i in test_col  if i not in train_col]);print('==' * 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>ID</th>\n",
       "      <th>join_date</th>\n",
       "      <th>sex</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>occupation_code</th>\n",
       "      <th>occupation_category_code</th>\n",
       "      <th>PCODE</th>\n",
       "      <th>ID X PCODE</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>611767</th>\n",
       "      <td>8SBA93U</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>1974</td>\n",
       "      <td>748L</td>\n",
       "      <td>2A7I</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>ECY3</td>\n",
       "      <td>8SBA93U X ECY3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611768</th>\n",
       "      <td>Y73ETTO</td>\n",
       "      <td>2018-01-11</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1987</td>\n",
       "      <td>XX25</td>\n",
       "      <td>Q6J6</td>\n",
       "      <td>90QI</td>\n",
       "      <td>ECY3</td>\n",
       "      <td>Y73ETTO X ECY3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611769</th>\n",
       "      <td>MVXG2LC</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1985</td>\n",
       "      <td>BOAS</td>\n",
       "      <td>6PE7</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>ECY3</td>\n",
       "      <td>MVXG2LC X ECY3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611770</th>\n",
       "      <td>BUQ8DQN</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1989</td>\n",
       "      <td>UAOD</td>\n",
       "      <td>2A7I</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>ECY3</td>\n",
       "      <td>BUQ8DQN X ECY3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611771</th>\n",
       "      <td>DEJBWYU</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1975</td>\n",
       "      <td>94KC</td>\n",
       "      <td>31JW</td>\n",
       "      <td>90QI</td>\n",
       "      <td>ECY3</td>\n",
       "      <td>DEJBWYU X ECY3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  join_date sex marital_status  birth_year branch_code  \\\n",
       "611767  8SBA93U 2019-01-09   F              M        1974        748L   \n",
       "611768  Y73ETTO 2018-01-11   M              M        1987        XX25   \n",
       "611769  MVXG2LC 2019-01-02   M              M        1985        BOAS   \n",
       "611770  BUQ8DQN 2019-01-07   M              M        1989        UAOD   \n",
       "611771  DEJBWYU 2020-01-03   M              M        1975        94KC   \n",
       "\n",
       "       occupation_code occupation_category_code PCODE      ID X PCODE  Label  \n",
       "611767            2A7I                     T4MS  ECY3  8SBA93U X ECY3      0  \n",
       "611768            Q6J6                     90QI  ECY3  Y73ETTO X ECY3      0  \n",
       "611769            6PE7                     T4MS  ECY3  MVXG2LC X ECY3      0  \n",
       "611770            2A7I                     T4MS  ECY3  BUQ8DQN X ECY3      0  \n",
       "611771            31JW                     90QI  ECY3  DEJBWYU X ECY3      0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>ID</th>\n",
       "      <th>join_date</th>\n",
       "      <th>sex</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>occupation_code</th>\n",
       "      <th>occupation_category_code</th>\n",
       "      <th>PCODE</th>\n",
       "      <th>ID X PCODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>209995</th>\n",
       "      <td>0GMU5UH</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>1986</td>\n",
       "      <td>748L</td>\n",
       "      <td>2A7I</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>ECY3</td>\n",
       "      <td>0GMU5UH X ECY3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209996</th>\n",
       "      <td>I9W11CD</td>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1980</td>\n",
       "      <td>UAOD</td>\n",
       "      <td>0KID</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>ECY3</td>\n",
       "      <td>I9W11CD X ECY3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209997</th>\n",
       "      <td>42WTEGT</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1993</td>\n",
       "      <td>748L</td>\n",
       "      <td>SST3</td>\n",
       "      <td>56SI</td>\n",
       "      <td>ECY3</td>\n",
       "      <td>42WTEGT X ECY3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209998</th>\n",
       "      <td>8EKC4O9</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>1981</td>\n",
       "      <td>49BM</td>\n",
       "      <td>2A7I</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>ECY3</td>\n",
       "      <td>8EKC4O9 X ECY3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209999</th>\n",
       "      <td>WHII93K</td>\n",
       "      <td>2018-01-11</td>\n",
       "      <td>M</td>\n",
       "      <td>U</td>\n",
       "      <td>1977</td>\n",
       "      <td>94KC</td>\n",
       "      <td>2A7I</td>\n",
       "      <td>T4MS</td>\n",
       "      <td>ECY3</td>\n",
       "      <td>WHII93K X ECY3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  join_date sex marital_status  birth_year branch_code  \\\n",
       "209995  0GMU5UH 2019-01-04   F              M        1986        748L   \n",
       "209996  I9W11CD 2019-01-08   M              M        1980        UAOD   \n",
       "209997  42WTEGT 2018-01-04   M              M        1993        748L   \n",
       "209998  8EKC4O9 2015-01-05   F              U        1981        49BM   \n",
       "209999  WHII93K 2018-01-11   M              U        1977        94KC   \n",
       "\n",
       "       occupation_code occupation_category_code PCODE      ID X PCODE  \n",
       "209995            2A7I                     T4MS  ECY3  0GMU5UH X ECY3  \n",
       "209996            0KID                     T4MS  ECY3  I9W11CD X ECY3  \n",
       "209997            SST3                     56SI  ECY3  42WTEGT X ECY3  \n",
       "209998            2A7I                     T4MS  ECY3  8EKC4O9 X ECY3  \n",
       "209999            2A7I                     T4MS  ECY3  WHII93K X ECY3  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LABEL ENCODE\n",
    "def encode_LE(train,test,cols,verbose=True):\n",
    "    for col in cols:\n",
    "        df_comb = pd.concat([train[col],test[col]],axis=0)\n",
    "        df_comb,_ = df_comb.factorize(sort=True)\n",
    "        nm = col\n",
    "        if df_comb.max()>32000: \n",
    "            train[nm] = df_comb[:len(train)].astype('int32')\n",
    "            test[nm] = df_comb[len(train):].astype('int32')\n",
    "        else:\n",
    "            train[nm] = df_comb[:len(train)].astype('int16')\n",
    "            test[nm] = df_comb[len(train):].astype('int16')\n",
    "        del df_comb; x=gc.collect()\n",
    "        if verbose: print(nm,', ',end='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID , branch_code , occupation_code , occupation_category_code , PCODE , sex , marital_status , "
     ]
    }
   ],
   "source": [
    "encode_LE(train, test, ['ID','branch_code', 'occupation_code','occupation_category_code','PCODE','sex','marital_status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>ID</th>\n",
       "      <th>join_date</th>\n",
       "      <th>sex</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>occupation_code</th>\n",
       "      <th>occupation_category_code</th>\n",
       "      <th>PCODE</th>\n",
       "      <th>ID X PCODE</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5384</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>4WKQSBB X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13834</td>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1981</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>CP5S02H X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3217</td>\n",
       "      <td>2013-01-06</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1991</td>\n",
       "      <td>3</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2YKDILJ X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3010</td>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>2S9E81J X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12546</td>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1990</td>\n",
       "      <td>3</td>\n",
       "      <td>157</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>BHDYVFT X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID  join_date  sex  marital_status  birth_year  branch_code  \\\n",
       "0   5384 2019-01-02    0               2        1987            0   \n",
       "1  13834 2019-01-06    0               2        1981           11   \n",
       "2   3217 2013-01-06    1               6        1991            3   \n",
       "3   3010 2019-01-08    1               2        1990            0   \n",
       "4  12546 2019-01-08    1               2        1990            3   \n",
       "\n",
       "   occupation_code  occupation_category_code  PCODE      ID X PCODE  Label  \n",
       "0               19                         5     15  4WKQSBB X P5DA      0  \n",
       "1               19                         5     15  CP5S02H X P5DA      0  \n",
       "2              185                         1     15  2YKDILJ X P5DA      0  \n",
       "3               82                         0     15  2S9E81J X P5DA      0  \n",
       "4              157                         5     15  BHDYVFT X P5DA      0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>ID</th>\n",
       "      <th>join_date</th>\n",
       "      <th>sex</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>occupation_code</th>\n",
       "      <th>occupation_category_code</th>\n",
       "      <th>PCODE</th>\n",
       "      <th>ID X PCODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16566</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1984</td>\n",
       "      <td>4</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>F86J5PC X P5DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18664</td>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>H6141K3 X P5DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29677</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1968</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>RBAYUXZ X P5DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22120</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1989</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>KCBILBQ X P5DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23700</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1982</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>LSEC1ZJ X P5DA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID  join_date  sex  marital_status  birth_year  branch_code  \\\n",
       "0  16566 2018-01-12    1               2        1984            4   \n",
       "1  18664 2019-01-10    1               2        1996            0   \n",
       "2  29677 2020-01-01    0               7        1968           11   \n",
       "3  22120 2019-01-02    1               2        1989            4   \n",
       "4  23700 2020-01-02    0               2        1982           11   \n",
       "\n",
       "   occupation_code  occupation_category_code  PCODE      ID X PCODE  \n",
       "0               96                         1     15  F86J5PC X P5DA  \n",
       "1              128                         1     15  H6141K3 X P5DA  \n",
       "2               19                         5     15  RBAYUXZ X P5DA  \n",
       "3               19                         5     15  KCBILBQ X P5DA  \n",
       "4                3                         5     15  LSEC1ZJ X P5DA  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inserting Year, month and day to the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# insert year, month and day to the dataframe\n",
    "def insert_YMD(meth):\n",
    "    \"\"\"\n",
    "        'meth' is the dataframe we are inserting year, month and day to.\n",
    "        This function also take care of nan value by filling it with the previous value\n",
    "    \"\"\"\n",
    "    meth.fillna(method='pad', inplace=True)\n",
    "    # Filling the nan values with the previous value\n",
    "    meth.insert(loc= 2, column= 'year', value=meth['join_date'].apply(lambda x: x.year))\n",
    "    # Returning the year of the join date\n",
    "    meth.insert(loc= 2, column= 'month', value=meth['join_date'].apply(lambda x: x.month))\n",
    "    # Returning the month of the join date\n",
    "    meth.insert(loc= 2, column= 'day', value=meth['join_date'].apply(lambda x: x.day))\n",
    "    # Returning the day of the join date\n",
    "# And everything is inplace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "insert_YMD(train)\n",
    "insert_YMD(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>join_date</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>occupation_code</th>\n",
       "      <th>occupation_category_code</th>\n",
       "      <th>PCODE</th>\n",
       "      <th>ID X PCODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16566</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1984</td>\n",
       "      <td>4</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>F86J5PC X P5DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18664</td>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>H6141K3 X P5DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29677</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1968</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>RBAYUXZ X P5DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22120</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1989</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>KCBILBQ X P5DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23700</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1982</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>LSEC1ZJ X P5DA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID  join_date  day  month  year  sex  marital_status  birth_year  \\\n",
       "0  16566 2018-01-12   12      1  2018    1               2        1984   \n",
       "1  18664 2019-01-10   10      1  2019    1               2        1996   \n",
       "2  29677 2020-01-01    1      1  2020    0               7        1968   \n",
       "3  22120 2019-01-02    2      1  2019    1               2        1989   \n",
       "4  23700 2020-01-02    2      1  2020    0               2        1982   \n",
       "\n",
       "   branch_code  occupation_code  occupation_category_code  PCODE  \\\n",
       "0            4               96                         1     15   \n",
       "1            0              128                         1     15   \n",
       "2           11               19                         5     15   \n",
       "3            4               19                         5     15   \n",
       "4           11                3                         5     15   \n",
       "\n",
       "       ID X PCODE  \n",
       "0  F86J5PC X P5DA  \n",
       "1  H6141K3 X P5DA  \n",
       "2  RBAYUXZ X P5DA  \n",
       "3  KCBILBQ X P5DA  \n",
       "4  LSEC1ZJ X P5DA  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL TRAINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "for date in (train, test):\n",
    "    date.drop(columns=['join_date'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>ID</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>occupation_code</th>\n",
       "      <th>occupation_category_code</th>\n",
       "      <th>PCODE</th>\n",
       "      <th>ID X PCODE</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5384</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>4WKQSBB X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13834</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1981</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>CP5S02H X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID  day  month  year  sex  marital_status  birth_year  branch_code  \\\n",
       "0   5384    2      1  2019    0               2        1987            0   \n",
       "1  13834    6      1  2019    0               2        1981           11   \n",
       "\n",
       "   occupation_code  occupation_category_code  PCODE      ID X PCODE  Label  \n",
       "0               19                         5     15  4WKQSBB X P5DA      0  \n",
       "1               19                         5     15  CP5S02H X P5DA      0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting data into training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train.drop(columns=['ID X PCODE', 'Label']), train['Label'], test_size=0.1, random_state=42)\n",
    "# Splitting the training and the testing set into 90% and 10% respectively with the random state of 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>ID</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>occupation_code</th>\n",
       "      <th>occupation_category_code</th>\n",
       "      <th>PCODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>372857</th>\n",
       "      <td>31953</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1988</td>\n",
       "      <td>13</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225504</th>\n",
       "      <td>5118</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1976</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566554</th>\n",
       "      <td>21158</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1987</td>\n",
       "      <td>3</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508755</th>\n",
       "      <td>760</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1963</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210775</th>\n",
       "      <td>19745</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID  day  month  year  sex  marital_status  birth_year  branch_code  \\\n",
       "372857  31953    1      1  2019    0               5        1988           13   \n",
       "225504   5118    7      1  2019    1               2        1976            4   \n",
       "566554  21158   10      1  2016    1               2        1987            3   \n",
       "508755    760   12      1  2019    0               2        1963            5   \n",
       "210775  19745    5      1  2018    1               2        1977            0   \n",
       "\n",
       "        occupation_code  occupation_category_code  PCODE  \n",
       "372857              168                         1      4  \n",
       "225504               19                         5     19  \n",
       "566554              185                         1      7  \n",
       "508755               19                         5     11  \n",
       "210775              200                         0     19  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the performance of each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from concurrent.futures import ThreadPoolExecutor\n",
    "from sklearn.metrics import classification_report\n",
    "def model_testing(md):\n",
    "    with ThreadPoolExecutor(max_workers=2) as executor:\n",
    "        executor.submit(md.fit, X_train, y_train)\n",
    "    print(\"Accuracy score:\", md.score(X_test, y_test), '\\n')\n",
    "    print(classification_report(y_test, md.predict(X_test)))\n",
    "    return md.predict(test.drop(columns=['ID X PCODE']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "log = LogisticRegression(solver='lbfgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.8911046454607866 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      1.00      0.94     54516\n",
      "           1       0.00      0.00      0.00      6662\n",
      "\n",
      "    accuracy                           0.89     61178\n",
      "   macro avg       0.45      0.50      0.47     61178\n",
      "weighted avg       0.79      0.89      0.84     61178\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = model_testing(log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.9422831736898886 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.97      0.97     54516\n",
      "           1       0.73      0.74      0.74      6662\n",
      "\n",
      "    accuracy                           0.94     61178\n",
      "   macro avg       0.85      0.85      0.85     61178\n",
      "weighted avg       0.94      0.94      0.94     61178\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = model_testing(dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using K Nearest Neighbor Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.8885383634639903 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      1.00      0.94     54516\n",
      "           1       0.11      0.00      0.01      6662\n",
      "\n",
      "    accuracy                           0.89     61178\n",
      "   macro avg       0.50      0.50      0.47     61178\n",
      "weighted avg       0.81      0.89      0.84     61178\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = model_testing(knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Naive bayes gaussian method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "naive = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.8910392624799764 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      1.00      0.94     54516\n",
      "           1       0.00      0.00      0.00      6662\n",
      "\n",
      "    accuracy                           0.89     61178\n",
      "   macro avg       0.45      0.50      0.47     61178\n",
      "weighted avg       0.79      0.89      0.84     61178\n",
      "\n"
     ]
    }
   ],
   "source": [
    "naive_pred = model_testing(naive)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Random forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "rd = RandomForestClassifier(n_estimators=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.9582529667527543 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98     54516\n",
      "           1       0.87      0.73      0.79      6662\n",
      "\n",
      "    accuracy                           0.96     61178\n",
      "   macro avg       0.92      0.86      0.88     61178\n",
      "weighted avg       0.96      0.96      0.96     61178\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = model_testing(rd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To save the predictions of RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "test.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data= pd.DataFrame(pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data.insert(loc=0, column='ID X PCODE', value=test['ID X PCODE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data.columns= ['ID X PCODE', 'Label']\n",
    "data.to_csv('Prediction.csv', index= False)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data['Label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "del data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Stacking method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A way of making use of different model to perform operation and make use of one model to do the final prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import StackingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "level0 = [('Logistic', LogisticRegression()), ('RandomForest', RandomForestClassifier()), ('DecisionTree', DecisionTreeClassifier()), ('Gaussian', GaussianNB()), ('knn', KNeighborsClassifier())]\n",
    "level1 = log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = StackingClassifier(estimators=level0, final_estimator=log, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StackingClassifier(cv=5,\n",
       "                   estimators=[('Logistic', LogisticRegression()),\n",
       "                               ('RandomForest', RandomForestClassifier()),\n",
       "                               ('DecisionTree', DecisionTreeClassifier()),\n",
       "                               ('Gaussian', GaussianNB()),\n",
       "                               ('knn', KNeighborsClassifier())],\n",
       "                   final_estimator=LogisticRegression())"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9607865572591454"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.9611625093988035 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98     54516\n",
      "           1       0.88      0.74      0.81      6662\n",
      "\n",
      "    accuracy                           0.96     61178\n",
      "   macro avg       0.93      0.86      0.89     61178\n",
      "weighted avg       0.96      0.96      0.96     61178\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gen_pred = model_testing(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_data= pd.DataFrame(gen_pred)\n",
    "gen_data.insert(loc=0, column='ID X PCODE', value=test['ID X PCODE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>ID X PCODE</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F86J5PC X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H6141K3 X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RBAYUXZ X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KCBILBQ X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSEC1ZJ X P5DA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID X PCODE  Label\n",
       "0  F86J5PC X P5DA      0\n",
       "1  H6141K3 X P5DA      0\n",
       "2  RBAYUXZ X P5DA      0\n",
       "3  KCBILBQ X P5DA      0\n",
       "4  LSEC1ZJ X P5DA      0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_data.columns= ['ID X PCODE', 'Label']\n",
    "gen_data.to_csv('Prediction.csv', index= False)\n",
    "gen_data.head()"
   ]
  }
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