1. Data Preparation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1f836f4",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d86dfe",
   "metadata": {},
   "source": [
    "Churn Prediction for Bank Customers merupakan perangkat analisa data untuk mengetahui nasabah yang churn berdasarkan profile nasabah bank yang telah tersedia.\n",
    "\n",
    "Kumpulan data ini berisi rincian nasabah bank dan variabel target adalah variabel biner (stay and churn) yang mencerminkan fakta apakah nasabah meninggalkan bank (menutup rekeningnya) atau tetap menjadi nasabah suatu bank.\n",
    "\n",
    "variabel fitur akan diproses guna memprediksi apakah nasabah akan menutup rekening atau tidak\n",
    "\n",
    "Dalam konteks bisnis, terutama saat menangani masalah prediksi churn (misalnya, ketika memprediksi pelanggan yang mungkin akan meninggalkan layanan), user seringkali lebih mementingkan untuk meminimalkan False Negative (FN) daripada False Positive (FP). Hal ini dikarenakan konsekuensi dari salah prediksi dalam kasus ini lebih berdampak pada bisnis.\n",
    "\n",
    "User menginginkan metrics bisnis berupa recall yakni lebih mengutamakan memprediksi churn namun sebenarnya tidak churn daripada memprediksi nasabah tidak churn sebenarnya nasabah tersebut churn, hal ini menunjukkan bahwa metode yang digunakan akan memperkecil nilai True Negative (TN) dan False Negative (FN), dan menaikkan TP (TP) dan False Positive (FP).\n",
    "\n",
    "Metrics bisnis Recall (dalam hal ini, mengurangi False Negative), metrics ini lebih fokus pada kemampuan model untuk mendeteksi dan mencegah pelanggan yang benar-benar akan churn (kasus positif) dari pada mengkhawatirkan seberapa banyak pelanggan yang tidak akan churn (kasus negatif) yang salah diprediksi sebagai churn."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0077f0a3",
   "metadata": {},
   "source": [
    "# Importasi Library"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ebe4d88",
   "metadata": {},
   "source": [
    "Melakukan importasi library yang dibutuhkan saat proses data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22c69d85-836a-49cb-9779-e8529e315e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import library untuk data preparation dan visualization\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# import warnings for ignore the warnings\n",
    "import warnings \n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# import pickle and json file for columns and model file\n",
    "import pickle\n",
    "import json\n",
    "import joblib\n",
    "import copy\n",
    "\n",
    "# Import train test split untuk splitting data\n",
    "from sklearn.model_selection import train_test_split\n",
    "import yaml\n",
    "import src.util as util\n",
    "from tqdm import tqdm\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d3a3eee2-f5b2-4692-8652-e48c8a69d754",
   "metadata": {},
   "outputs": [],
   "source": [
    "config_data = util.load_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30d8fade",
   "metadata": {},
   "source": [
    "# Read Data - Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a947cd08",
   "metadata": {},
   "source": [
    "Churn prediction saat ini menggunakan data yang disadur dari kaggle dengan laman :\n",
    " * https://www.kaggle.com/datasets/shrutimechlearn/churn-modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bae6bbdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_raw_data(config: dict) -> pd.DataFrame:\n",
    "    # Create variable to store raw dataset\n",
    "    raw_dataset = pd.DataFrame()\n",
    "\n",
    "    # Raw dataset dir\n",
    "    raw_dataset_dir = config[\"raw_dataset_dir\"]\n",
    "\n",
    "    # Look and load add CSV files\n",
    "    for i in tqdm(os.listdir(raw_dataset_dir)):\n",
    "        raw_dataset = pd.concat([pd.read_csv(raw_dataset_dir + i), raw_dataset])\n",
    "    \n",
    "    # Return raw dataset\n",
    "    return raw_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "02dd8fc4-32c9-471c-90cf-1dda67782129",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 41.72it/s]\n"
     ]
    }
   ],
   "source": [
    "raw_dataset = read_raw_data(config_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1484f765-6642-47c1-b1af-53d01b6ef4e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Geography        10000 non-null  object \n",
      " 5   Gender           10000 non-null  object \n",
      " 6   Age              10000 non-null  int64  \n",
      " 7   Tenure           10000 non-null  int64  \n",
      " 8   Balance          10000 non-null  float64\n",
      " 9   NumOfProducts    10000 non-null  int64  \n",
      " 10  HasCrCard        10000 non-null  int64  \n",
      " 11  IsActiveMember   10000 non-null  int64  \n",
      " 12  EstimatedSalary  10000 non-null  float64\n",
      " 13  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(9), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "raw_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a03b2519-a7de-423f-965f-41104f496da8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 21.92it/s]\n"
     ]
    }
   ],
   "source": [
    "raw_dataset = read_raw_data(config_data).drop([\"RowNumber\",\"CustomerId\",\"Surname\"], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38d83bab",
   "metadata": {},
   "source": [
    "Menghapus kolom yang tidak diperlukan dalam proses analisa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "963590d0",
   "metadata": {},
   "source": [
    "Sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5cca6f4d",
   "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0             619    France  Female   42       2       0.00              1   \n",
       "1             608     Spain  Female   41       1   83807.86              1   \n",
       "2             502    France  Female   42       8  159660.80              3   \n",
       "3             699    France  Female   39       1       0.00              2   \n",
       "4             850     Spain  Female   43       2  125510.82              1   \n",
       "...           ...       ...     ...  ...     ...        ...            ...   \n",
       "9995          771    France    Male   39       5       0.00              2   \n",
       "9996          516    France    Male   35      10   57369.61              1   \n",
       "9997          709    France  Female   36       7       0.00              1   \n",
       "9998          772   Germany    Male   42       3   75075.31              2   \n",
       "9999          792    France  Female   28       4  130142.79              1   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0             1               1        101348.88       1  \n",
       "1             0               1        112542.58       0  \n",
       "2             1               0        113931.57       1  \n",
       "3             0               0         93826.63       0  \n",
       "4             1               1         79084.10       0  \n",
       "...         ...             ...              ...     ...  \n",
       "9995          1               0         96270.64       0  \n",
       "9996          1               1        101699.77       0  \n",
       "9997          0               1         42085.58       1  \n",
       "9998          1               0         92888.52       1  \n",
       "9999          1               0         38190.78       0  \n",
       "\n",
       "[10000 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sanity check data churn bank yang telah dihapus fitur yang tidak digunakan\n",
    "raw_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b882fe8-527f-4f53-93cb-a3e8984a3a43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   CreditScore      10000 non-null  int64  \n",
      " 1   Geography        10000 non-null  object \n",
      " 2   Gender           10000 non-null  object \n",
      " 3   Age              10000 non-null  int64  \n",
      " 4   Tenure           10000 non-null  int64  \n",
      " 5   Balance          10000 non-null  float64\n",
      " 6   NumOfProducts    10000 non-null  int64  \n",
      " 7   HasCrCard        10000 non-null  int64  \n",
      " 8   IsActiveMember   10000 non-null  int64  \n",
      " 9   EstimatedSalary  10000 non-null  float64\n",
      " 10  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(7), object(2)\n",
      "memory usage: 859.5+ KB\n"
     ]
    }
   ],
   "source": [
    "raw_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2854cf1d",
   "metadata": {},
   "source": [
    "# Data Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cad43e68",
   "metadata": {},
   "source": [
    "Data yang digunakan merupakan data yang menggambarkan profile nasabah yang menutup rekening dan nasabah yang tetap. adapun fitur profile nasabah yang digunakan antara lain\n",
    "\n",
    "Data berisi 10000 baris dan 11 kolom yaitu :\n",
    "\n",
    "* CreditScore : Skor Kredit yang diukur oleh pihak bank sebagai penilaian terhadap nasabah untuk menggambarkan risiko kredit seorang calon debitur\n",
    "* Geography : Negara asal nasabah\n",
    "* Gender : Jenis Kelamin nasabah\n",
    "* Tenure : Jumlah tahun kepesertaan nasabah\n",
    "* Balance : Saldo bank nasabah\n",
    "* NumOfProducts : Jumlah produk bank yang digunakan nasabah\n",
    "* HasCrCard : apakah nasabah memegang kartu kredit dengan bank atau tidak\n",
    "* IsActiveMember : apakah nasabah adalah anggota aktif di bank atau tidak \n",
    "* EstimatedSalary : Perkiraan gaji nasabah dalam Dolar\n",
    "* Exited : 1 jika nasabah menutup rekening dengan bank dan 0 jika nasabah dipertahankan"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f0d9b3ee",
   "metadata": {},
   "source": [
    "CreditScore       :\n",
    "    [int64]\n",
    "    [350 - 850]\n",
    "    Skor Kredit yang diukur oleh pihak bank sebagai penilaian terhadap nasabah untuk menggambarkan \n",
    "    risiko kredit seorang calon debitur\n",
    "\n",
    "Geography         :\n",
    "    [object]\n",
    "    ['France', 'Spain', 'Germany']\n",
    "    Negara asal nasabah\n",
    "\n",
    "Gender            :\n",
    "    [object]\n",
    "    ['male','female']\n",
    "    Jenis Kelamin nasabah\n",
    "    \n",
    "Age               :\n",
    "    [int64]\n",
    "    [18 - 92]\n",
    "    Jenis Kelamin nasabah\n",
    "\n",
    "Tenure            :\n",
    "    [int64]\n",
    "    [0 - 10]\n",
    "    Jumlah tahun kepesertaan nasabah\n",
    "\n",
    "Balance            :\n",
    "    [float64]\n",
    "    [0.0 - 250898.09]\n",
    "    Saldo bank nasabah\n",
    "\n",
    "NumOfProducts      :\n",
    "    [int64]\n",
    "    [1, 3, 2, 4]\n",
    "    Jumlah produk bank yang digunakan nasabah\n",
    "\n",
    "HasCrCard           :\n",
    "    [int64]\n",
    "    [0 - 1]\n",
    "    apakah nasabah memegang kartu kredit dengan bank atau tidak\n",
    "\n",
    "IsActiveMember      :\n",
    "    [int64]\n",
    "    [0 - 10]\n",
    "    apakah nasabah adalah anggota aktif di bank atau tidak \n",
    "\n",
    "EstimatedSalary     :\n",
    "    [float64]\n",
    "    [11.58 - 199992.48]\n",
    "    Perkiraan gaji nasabah dalam Dolar\n",
    "\n",
    "Exited              :\n",
    "    [int64]\n",
    "    [0,1]\n",
    "    1 jika nasabah menutup rekening dengan bank dan 0 jika nasabah dipertahankan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fbc79a1",
   "metadata": {},
   "source": [
    "# Data Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c38c247",
   "metadata": {},
   "source": [
    "### Data Type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a538f159",
   "metadata": {},
   "source": [
    "Mengecek tipe data apakah sudah sesuai dengan fitur dalam proses analisa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61359e06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cek tipe data dengan menggunakan info()\n",
    "raw_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cd17979",
   "metadata": {},
   "source": [
    "### Check Nilai Null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "594a961a",
   "metadata": {},
   "source": [
    "Mengecek apakah data terdapat nilai null pada setiap kolom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b5c4f5a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Menghitung nilai null pada kolom\n",
    "raw_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abb72fac",
   "metadata": {},
   "source": [
    "### Melihat statistik deskriptif dan Range Data Numerikal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dabce131",
   "metadata": {},
   "source": [
    "Cek statistika deskriptif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2d1137c7",
   "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>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>650.528800</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>0.70550</td>\n",
       "      <td>0.515100</td>\n",
       "      <td>100090.239881</td>\n",
       "      <td>0.203700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>96.653299</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>0.45584</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>0.402769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>584.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51002.110000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>652.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>97198.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100193.915000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>718.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>127644.240000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149388.247500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>850.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>250898.090000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199992.480000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CreditScore           Age        Tenure        Balance  NumOfProducts  \\\n",
       "count  10000.000000  10000.000000  10000.000000   10000.000000   10000.000000   \n",
       "mean     650.528800     38.921800      5.012800   76485.889288       1.530200   \n",
       "std       96.653299     10.487806      2.892174   62397.405202       0.581654   \n",
       "min      350.000000     18.000000      0.000000       0.000000       1.000000   \n",
       "25%      584.000000     32.000000      3.000000       0.000000       1.000000   \n",
       "50%      652.000000     37.000000      5.000000   97198.540000       1.000000   \n",
       "75%      718.000000     44.000000      7.000000  127644.240000       2.000000   \n",
       "max      850.000000     92.000000     10.000000  250898.090000       4.000000   \n",
       "\n",
       "         HasCrCard  IsActiveMember  EstimatedSalary        Exited  \n",
       "count  10000.00000    10000.000000     10000.000000  10000.000000  \n",
       "mean       0.70550        0.515100    100090.239881      0.203700  \n",
       "std        0.45584        0.499797     57510.492818      0.402769  \n",
       "min        0.00000        0.000000        11.580000      0.000000  \n",
       "25%        0.00000        0.000000     51002.110000      0.000000  \n",
       "50%        1.00000        1.000000    100193.915000      0.000000  \n",
       "75%        1.00000        1.000000    149388.247500      0.000000  \n",
       "max        1.00000        1.000000    199992.480000      1.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Melihat statistika deskriptif dengan describe()\n",
    "raw_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1e4496dd-9e24-41c2-9e6a-17bc26b72895",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_data(input_data, params):\n",
    "    # Check data types\n",
    "    assert input_data.select_dtypes(\"float\").columns.to_list() == params[\"float64_columns\"], \"an error occurs in datetime column(s).\"\n",
    "    assert input_data.select_dtypes(\"object\").columns.to_list() == params[\"object_columns\"], \"an error occurs in object column(s).\"\n",
    "\n",
    "    # Check range of data\n",
    "    assert set(input_data.Geography).issubset(set(params[\"range_Geography\"])), \"an error occurs in stasiun range.\"\n",
    "    assert set(input_data.Gender).issubset(set(params[\"range_Gender\"])), \"an error occurs in stasiun range.\"\n",
    "    assert input_data.CreditScore.between(params[\"range_CreditScore\"][0], params[\"range_CreditScore\"][1]).sum() == len(input_data), \"an error occurs in pm10 range.\"\n",
    "    assert input_data.Age.between(params[\"range_Age\"][0], params[\"range_Age\"][1]).sum() == len(input_data), \"an error occurs in pm25 range.\"\n",
    "    assert input_data.Tenure.between(params[\"range_Tenure\"][0], params[\"range_Tenure\"][1]).sum() == len(input_data), \"an error occurs in so2 range.\"\n",
    "    assert input_data.Balance.between(params[\"range_Balance\"][0], params[\"range_Balance\"][1]).sum() == len(input_data), \"an error occurs in co range.\"\n",
    "    assert input_data.NumOfProducts.between(params[\"range_NumOfProducts\"][0], params[\"range_NumOfProducts\"][1]).sum() == len(input_data), \"an error occurs in o3 range.\"\n",
    "    assert input_data.HasCrCard.between(params[\"range_HasCrCard\"][0], params[\"range_HasCrCard\"][1]).sum() == len(input_data), \"an error occurs in no2 range.\"\n",
    "    assert input_data.IsActiveMember.between(params[\"range_IsActiveMember\"][0], params[\"range_IsActiveMember\"][1]).sum() == len(input_data), \"an error occurs in no2 range.\"\n",
    "    assert input_data.EstimatedSalary.between(params[\"range_EstimatedSalary\"][0], params[\"range_EstimatedSalary\"][1]).sum() == len(input_data), \"an error occurs in o3 range.\"\n",
    "    assert input_data.Exited.between(params[\"Exited_categories\"][0], params[\"Exited_categories\"][1]).sum() == len(input_data), \"an error occurs in no2 range.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "66f8c001-30d6-44f6-9fbb-0e81906c21f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "check_data(raw_dataset, config_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b81f517",
   "metadata": {},
   "source": [
    "### Melihat Dimensi Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0b66b235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 11)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "887a431c",
   "metadata": {},
   "source": [
    "# Data Splitting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa98f66",
   "metadata": {},
   "source": [
    "Melakukan spliting data untuk memisahkan set training, set validation dan set test yang terbagi atas variabel x dan y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6deade50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pemisahan Variabel X dan Y\n",
    "X = raw_dataset.drop(columns = \"Exited\")\n",
    "y = raw_dataset[\"Exited\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a96df1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split Data 80% training 20% testing\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size = 0.3,\n",
    "                                                    random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "133bc8bf-4b0b-433b-b2fe-4b90da0568cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3144</th>\n",
       "      <td>648</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>81370.07</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>181534.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9939</th>\n",
       "      <td>693</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>57</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>135502.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7925</th>\n",
       "      <td>586</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>168261.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>438</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>78398.69</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>44937.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9415</th>\n",
       "      <td>768</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>129264.05</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19150.14</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9785</th>\n",
       "      <td>455</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>155879.09</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70774.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7763</th>\n",
       "      <td>614</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>44054.84</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>73329.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5218</th>\n",
       "      <td>685</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>104719.94</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38691.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>643</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>94443.77</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>165614.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3582</th>\n",
       "      <td>686</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8816.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "3144          648     Spain    Male   55       1   81370.07              1   \n",
       "9939          693     Spain  Female   57       9       0.00              2   \n",
       "7925          586     Spain  Female   33       7       0.00              2   \n",
       "309           438   Germany    Male   31       8   78398.69              1   \n",
       "9415          768   Germany  Female   43       2  129264.05              2   \n",
       "...           ...       ...     ...  ...     ...        ...            ...   \n",
       "9785          455    France  Female   27       5  155879.09              2   \n",
       "7763          614     Spain  Female   36       1   44054.84              1   \n",
       "5218          685    France    Male   20       4  104719.94              2   \n",
       "1346          643    France    Male   30       5   94443.77              1   \n",
       "3582          686    France    Male   35       4       0.00              1   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  \n",
       "3144          0               1        181534.04  \n",
       "9939          1               1        135502.77  \n",
       "7925          1               1        168261.40  \n",
       "309           1               0         44937.01  \n",
       "9415          0               0         19150.14  \n",
       "...         ...             ...              ...  \n",
       "9785          0               0         70774.97  \n",
       "7763          1               1         73329.08  \n",
       "5218          1               0         38691.34  \n",
       "1346          1               1        165614.40  \n",
       "3582          1               0          8816.37  \n",
       "\n",
       "[7000 rows x 10 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e68d5ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data train menjadi train dan validation set\n",
    "X_test, X_valid, y_test, y_valid = train_test_split(X_test, y_test, \n",
    "                                                    test_size=0.5, \n",
    "                                                    random_state=42,\n",
    "                                                    stratify = y_test\n",
    "                                                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0c2cd6a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3144</th>\n",
       "      <td>648</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>81370.07</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>181534.04</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9939</th>\n",
       "      <td>693</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>57</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>135502.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7925</th>\n",
       "      <td>586</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>168261.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>438</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>78398.69</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>44937.01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9415</th>\n",
       "      <td>768</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>129264.05</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19150.14</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9785</th>\n",
       "      <td>455</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>155879.09</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70774.97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7763</th>\n",
       "      <td>614</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>44054.84</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>73329.08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5218</th>\n",
       "      <td>685</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>104719.94</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38691.34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>643</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>94443.77</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>165614.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3582</th>\n",
       "      <td>686</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8816.37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "3144          648     Spain    Male   55       1   81370.07              1   \n",
       "9939          693     Spain  Female   57       9       0.00              2   \n",
       "7925          586     Spain  Female   33       7       0.00              2   \n",
       "309           438   Germany    Male   31       8   78398.69              1   \n",
       "9415          768   Germany  Female   43       2  129264.05              2   \n",
       "...           ...       ...     ...  ...     ...        ...            ...   \n",
       "9785          455    France  Female   27       5  155879.09              2   \n",
       "7763          614     Spain  Female   36       1   44054.84              1   \n",
       "5218          685    France    Male   20       4  104719.94              2   \n",
       "1346          643    France    Male   30       5   94443.77              1   \n",
       "3582          686    France    Male   35       4       0.00              1   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "3144          0               1        181534.04       0  \n",
       "9939          1               1        135502.77       0  \n",
       "7925          1               1        168261.40       0  \n",
       "309           1               0         44937.01       0  \n",
       "9415          0               0         19150.14       0  \n",
       "...         ...             ...              ...     ...  \n",
       "9785          0               0         70774.97       0  \n",
       "7763          1               1         73329.08       0  \n",
       "5218          1               0         38691.34       0  \n",
       "1346          1               1        165614.40       0  \n",
       "3582          1               0          8816.37       0  \n",
       "\n",
       "[7000 rows x 11 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Menggabungkan x train dan y train untuk keperluan EDA\n",
    "data_bank = pd.merge(X_train, y_train, left_index=True, right_index=True)\n",
    "#Sanity Check Data\n",
    "data_bank"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a359e7",
   "metadata": {},
   "source": [
    "## Final Result - Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5133f789",
   "metadata": {},
   "source": [
    "Ekspor Hasil data preparation dengan file pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bf9617e4-cf4a-4da8-8e2b-cd83db15ed25",
   "metadata": {},
   "outputs": [],
   "source": [
    "util.pickle_dump(X_train, config_data[\"train_set_path\"][0])\n",
    "util.pickle_dump(y_train, config_data[\"train_set_path\"][1])\n",
    "\n",
    "util.pickle_dump(X_valid, config_data[\"valid_set_path\"][0])\n",
    "util.pickle_dump(y_valid, config_data[\"valid_set_path\"][1])\n",
    "\n",
    "util.pickle_dump(X_test, config_data[\"test_set_path\"][0])\n",
    "util.pickle_dump(y_test, config_data[\"test_set_path\"][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ac528f3-382b-4bf1-95b9-d1d49231888e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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