{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# i. Perkenalan "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Graded Challenge 5\n",
    "\n",
    "Nama  : Kenneth Vincentius\n",
    "\n",
    "Batch : HCK-007\n",
    "\n",
    "Membuat Model dimana untuk mempredik apakah user bisa atau tidak membayar untuk bulan depan berdasarkan history data pembayaran user pada bulan-bulan sebelumnya\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Latar Belakang : Saya adalah seorang data sientict di PT. ABC dan saya diberikan proyek untuk mengelola data set tentang credit score pembayaran user mereka. Saya diminta untuk memilih model yang tepat yang tepat untuk digunakan pada dataset ini dan saya juga harus memberikan prediksi terhadap user-user yang ada apakah mereka bisa membayar untuk bulan berikutnya atau tidak berdasarkan dari data history mereka sebelum-sebelumnya"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement : Memprediksi kemampuan user untuk membayar sehingga dapat mengurangi user yang tidak mampu bayar sebanyak 10% dalam waktu 3 bulan untuk mengurangi kerugian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ii. SQL Query"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Syntax SQL untuk memilih kolom mana saja yang akan digunakan untuk "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```sql\n",
    "SELECT\n",
    "    limit_balance,\n",
    "    CAST(sex AS INT64) AS sex,\n",
    "    CAST(education_level AS INT64) AS education_level,\n",
    "    CAST(marital_status AS INT64) AS marital_status,\n",
    "    age,\n",
    "    pay_0,\n",
    "    pay_2,\n",
    "    pay_3,\n",
    "    pay_4,\n",
    "    CAST(pay_5 AS FLOAT64) AS pay_5,\n",
    "    CAST(pay_6 AS FLOAT64) AS pay_6,\n",
    "    bill_amt_1,\n",
    "    bill_amt_2,\n",
    "    bill_amt_3,\n",
    "    bill_amt_4,\n",
    "    bill_amt_5,\n",
    "    bill_amt_6,\n",
    "    pay_amt_1,\n",
    "    pay_amt_2,\n",
    "    pay_amt_3,\n",
    "    pay_amt_4,\n",
    "    pay_amt_5,\n",
    "    pay_amt_6,\n",
    "    CAST(default_payment_next_month AS INT64) AS default_payment_next_month\n",
    "FROM `bigquery-public-data.ml_datasets.credit_card_default`\n",
    "LIMIT 19950;\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# iii. Import Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from scipy.stats import kendalltau\n",
    "import pickle\n",
    "\n",
    "\n",
    "# preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from feature_engine.outliers import Winsorizer , OutlierTrimmer\n",
    "from imblearn.over_sampling import SMOTENC\n",
    "\n",
    "# model \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay, f1_score, accuracy_score, precision_score, recall_score\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# iv. Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading data csv yang sudah disimpan ke dalam bentuk csv dari bigquery\n",
    "df=pd.read_csv('P1G1_Kenneth Vincentius.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>limit_balance</th>\n",
       "      <th>sex</th>\n",
       "      <th>education_level</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>age</th>\n",
       "      <th>pay_0</th>\n",
       "      <th>pay_2</th>\n",
       "      <th>pay_3</th>\n",
       "      <th>pay_4</th>\n",
       "      <th>pay_5</th>\n",
       "      <th>...</th>\n",
       "      <th>bill_amt_4</th>\n",
       "      <th>bill_amt_5</th>\n",
       "      <th>bill_amt_6</th>\n",
       "      <th>pay_amt_1</th>\n",
       "      <th>pay_amt_2</th>\n",
       "      <th>pay_amt_3</th>\n",
       "      <th>pay_amt_4</th>\n",
       "      <th>pay_amt_5</th>\n",
       "      <th>pay_amt_6</th>\n",
       "      <th>default_payment_next_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29296.0</td>\n",
       "      <td>26210.0</td>\n",
       "      <td>17643.0</td>\n",
       "      <td>2545.0</td>\n",
       "      <td>2208.0</td>\n",
       "      <td>1336.0</td>\n",
       "      <td>2232.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50146.0</td>\n",
       "      <td>50235.0</td>\n",
       "      <td>48984.0</td>\n",
       "      <td>1689.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>3480.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1434.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4641.0</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>260000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27821.0</td>\n",
       "      <td>30767.0</td>\n",
       "      <td>29890.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1137.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>150464.0</td>\n",
       "      <td>143375.0</td>\n",
       "      <td>146411.0</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>146896.0</td>\n",
       "      <td>157436.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>4709.0</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>300000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>65150.0</td>\n",
       "      <td>-450.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>15235.0</td>\n",
       "      <td>1491.0</td>\n",
       "      <td>1303.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>130000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>62377.0</td>\n",
       "      <td>63832.0</td>\n",
       "      <td>65099.0</td>\n",
       "      <td>2886.0</td>\n",
       "      <td>2908.0</td>\n",
       "      <td>2129.0</td>\n",
       "      <td>2354.0</td>\n",
       "      <td>2366.0</td>\n",
       "      <td>2291.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>200000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>124647.0</td>\n",
       "      <td>126921.0</td>\n",
       "      <td>129167.0</td>\n",
       "      <td>7822.0</td>\n",
       "      <td>4417.0</td>\n",
       "      <td>4446.0</td>\n",
       "      <td>4597.0</td>\n",
       "      <td>4677.0</td>\n",
       "      <td>4698.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>174500.0</td>\n",
       "      <td>137406.0</td>\n",
       "      <td>204975.0</td>\n",
       "      <td>54209.0</td>\n",
       "      <td>4607.0</td>\n",
       "      <td>4603.0</td>\n",
       "      <td>5224.0</td>\n",
       "      <td>207440.0</td>\n",
       "      <td>7509.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>230000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>105508.0</td>\n",
       "      <td>108101.0</td>\n",
       "      <td>110094.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>6607.0</td>\n",
       "      <td>3773.0</td>\n",
       "      <td>4290.0</td>\n",
       "      <td>4164.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   limit_balance  sex  education_level  marital_status   age  pay_0  pay_2  \\\n",
       "0        80000.0    1                6               1  54.0    0.0    0.0   \n",
       "1       200000.0    1                4               1  49.0    0.0    0.0   \n",
       "2        20000.0    2                6               2  22.0    0.0    0.0   \n",
       "3       260000.0    2                4               2  33.0    0.0    0.0   \n",
       "4       150000.0    1                4               2  32.0    0.0    0.0   \n",
       "5       300000.0    2                4               2  32.0    0.0    0.0   \n",
       "6       130000.0    1                1               1  45.0    0.0    0.0   \n",
       "7       200000.0    1                1               1  58.0    0.0    0.0   \n",
       "8       500000.0    1                1               1  39.0    0.0    0.0   \n",
       "9       230000.0    1                1               1  48.0    0.0    0.0   \n",
       "\n",
       "   pay_3  pay_4  pay_5  ...  bill_amt_4  bill_amt_5  bill_amt_6  pay_amt_1  \\\n",
       "0    0.0    0.0    0.0  ...     29296.0     26210.0     17643.0     2545.0   \n",
       "1    0.0    0.0    0.0  ...     50146.0     50235.0     48984.0     1689.0   \n",
       "2    0.0    0.0    0.0  ...      1434.0       500.0         0.0     4641.0   \n",
       "3    0.0    0.0    0.0  ...     27821.0     30767.0     29890.0     5000.0   \n",
       "4    0.0   -1.0    0.0  ...    150464.0    143375.0    146411.0     4019.0   \n",
       "5    0.0    0.0    0.0  ...     65150.0      -450.0       700.0    15235.0   \n",
       "6    0.0    0.0    0.0  ...     62377.0     63832.0     65099.0     2886.0   \n",
       "7    0.0    0.0    0.0  ...    124647.0    126921.0    129167.0     7822.0   \n",
       "8    0.0    0.0    0.0  ...    174500.0    137406.0    204975.0    54209.0   \n",
       "9    0.0    0.0    0.0  ...    105508.0    108101.0    110094.0     7000.0   \n",
       "\n",
       "   pay_amt_2  pay_amt_3  pay_amt_4  pay_amt_5  pay_amt_6  \\\n",
       "0     2208.0     1336.0     2232.0      542.0      348.0   \n",
       "1     2164.0     2500.0     3480.0     2500.0     3000.0   \n",
       "2     1019.0      900.0        0.0     1500.0        0.0   \n",
       "3     5000.0     1137.0     5000.0     1085.0     5000.0   \n",
       "4   146896.0   157436.0     4600.0     4709.0     5600.0   \n",
       "5     1491.0     1303.0        0.0     2000.0     1400.0   \n",
       "6     2908.0     2129.0     2354.0     2366.0     2291.0   \n",
       "7     4417.0     4446.0     4597.0     4677.0     4698.0   \n",
       "8     4607.0     4603.0     5224.0   207440.0     7509.0   \n",
       "9     6607.0     3773.0     4290.0     4164.0     2000.0   \n",
       "\n",
       "   default_payment_next_month  \n",
       "0                           1  \n",
       "1                           0  \n",
       "2                           1  \n",
       "3                           0  \n",
       "4                           0  \n",
       "5                           0  \n",
       "6                           0  \n",
       "7                           0  \n",
       "8                           0  \n",
       "9                           0  \n",
       "\n",
       "[10 rows x 24 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#menampilkan 10 data teratas \n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>pay_amt_5</th>\n",
       "      <th>pay_amt_6</th>\n",
       "      <th>default_payment_next_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2955</th>\n",
       "      <td>360000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>463.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2956</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2377.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2958</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>5880.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2963</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>44.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2964</th>\n",
       "      <td>290000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3184.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>3184.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>6617.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      limit_balance  sex  education_level  marital_status   age  pay_0  pay_2  \\\n",
       "2955       360000.0    2                2               2  26.0   -1.0   -1.0   \n",
       "2956       100000.0    1                3               1  40.0    0.0    0.0   \n",
       "2957        30000.0    2                3               1  48.0    1.0   -1.0   \n",
       "2958        80000.0    2                3               1  39.0   -1.0   -1.0   \n",
       "2959        20000.0    1                3               2  26.0   -1.0   -1.0   \n",
       "2960        80000.0    2                3               2  28.0   -1.0   -1.0   \n",
       "2961        50000.0    2                3               1  51.0   -1.0   -1.0   \n",
       "2962       450000.0    2                2               1  38.0   -2.0   -2.0   \n",
       "2963        50000.0    2                2               1  44.0   -2.0   -2.0   \n",
       "2964       290000.0    2                2               1  39.0    1.0   -2.0   \n",
       "\n",
       "      pay_3  pay_4  pay_5  ...  bill_amt_4  bill_amt_5  bill_amt_6  pay_amt_1  \\\n",
       "2955   -1.0   -1.0   -2.0  ...         0.0         0.0         0.0      463.0   \n",
       "2956   -1.0   -1.0   -2.0  ...         0.0         0.0         0.0     2000.0   \n",
       "2957   -1.0   -2.0   -2.0  ...         0.0         0.0         0.0      200.0   \n",
       "2958   -1.0   -1.0   -2.0  ...         0.0         0.0      5000.0     5000.0   \n",
       "2959   -1.0   -2.0   -2.0  ...         0.0         0.0         0.0     1560.0   \n",
       "2960   -1.0   -2.0   -2.0  ...         0.0         0.0         0.0     2800.0   \n",
       "2961   -1.0   -1.0   -2.0  ...         0.0         0.0         0.0      300.0   \n",
       "2962   -2.0   -2.0   -2.0  ...       390.0       390.0       390.0      390.0   \n",
       "2963   -2.0   -2.0   -2.0  ...       390.0       390.0         0.0      390.0   \n",
       "2964   -2.0   -2.0   -2.0  ...      3184.0       390.0       390.0    10000.0   \n",
       "\n",
       "      pay_amt_2  pay_amt_3  pay_amt_4  pay_amt_5  pay_amt_6  \\\n",
       "2955     2500.0        0.0        0.0        0.0        0.0   \n",
       "2956     2377.0    40000.0        0.0        0.0        0.0   \n",
       "2957        0.0        0.0        0.0        0.0        0.0   \n",
       "2958     5000.0        0.0     5000.0     5000.0      470.0   \n",
       "2959        0.0        0.0        0.0        0.0        0.0   \n",
       "2960        0.0        0.0        0.0        0.0        0.0   \n",
       "2961     5880.0        0.0        0.0        0.0        0.0   \n",
       "2962      780.0      390.0      390.0      390.0      390.0   \n",
       "2963      390.0      390.0      390.0        0.0      780.0   \n",
       "2964      800.0     3184.0      390.0      390.0     6617.0   \n",
       "\n",
       "      default_payment_next_month  \n",
       "2955                           0  \n",
       "2956                           0  \n",
       "2957                           0  \n",
       "2958                           0  \n",
       "2959                           0  \n",
       "2960                           0  \n",
       "2961                           1  \n",
       "2962                           1  \n",
       "2963                           0  \n",
       "2964                           0  \n",
       "\n",
       "[10 rows x 24 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#menampilkan 10 data terbawah\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# v. Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2965 entries, 0 to 2964\n",
      "Data columns (total 24 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   limit_balance               2965 non-null   float64\n",
      " 1   sex                         2965 non-null   int64  \n",
      " 2   education_level             2965 non-null   int64  \n",
      " 3   marital_status              2965 non-null   int64  \n",
      " 4   age                         2965 non-null   float64\n",
      " 5   pay_0                       2965 non-null   float64\n",
      " 6   pay_2                       2965 non-null   float64\n",
      " 7   pay_3                       2965 non-null   float64\n",
      " 8   pay_4                       2965 non-null   float64\n",
      " 9   pay_5                       2965 non-null   float64\n",
      " 10  pay_6                       2965 non-null   float64\n",
      " 11  bill_amt_1                  2965 non-null   float64\n",
      " 12  bill_amt_2                  2965 non-null   float64\n",
      " 13  bill_amt_3                  2965 non-null   float64\n",
      " 14  bill_amt_4                  2965 non-null   float64\n",
      " 15  bill_amt_5                  2965 non-null   float64\n",
      " 16  bill_amt_6                  2965 non-null   float64\n",
      " 17  pay_amt_1                   2965 non-null   float64\n",
      " 18  pay_amt_2                   2965 non-null   float64\n",
      " 19  pay_amt_3                   2965 non-null   float64\n",
      " 20  pay_amt_4                   2965 non-null   float64\n",
      " 21  pay_amt_5                   2965 non-null   float64\n",
      " 22  pay_amt_6                   2965 non-null   float64\n",
      " 23  default_payment_next_month  2965 non-null   int64  \n",
      "dtypes: float64(20), int64(4)\n",
      "memory usage: 556.1 KB\n"
     ]
    }
   ],
   "source": [
    "#menampilkan detail kolom\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "limit_balance                 0\n",
       "sex                           0\n",
       "education_level               0\n",
       "marital_status                0\n",
       "age                           0\n",
       "pay_0                         0\n",
       "pay_2                         0\n",
       "pay_3                         0\n",
       "pay_4                         0\n",
       "pay_5                         0\n",
       "pay_6                         0\n",
       "bill_amt_1                    0\n",
       "bill_amt_2                    0\n",
       "bill_amt_3                    0\n",
       "bill_amt_4                    0\n",
       "bill_amt_5                    0\n",
       "bill_amt_6                    0\n",
       "pay_amt_1                     0\n",
       "pay_amt_2                     0\n",
       "pay_amt_3                     0\n",
       "pay_amt_4                     0\n",
       "pay_amt_5                     0\n",
       "pay_amt_6                     0\n",
       "default_payment_next_month    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mencek apakah ada data yang missing value\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merename kolom agar lebih singkat dan sesuai urutan\n",
    "df.rename(columns={'default_payment_next_month':'def_payment'}, inplace=True)\n",
    "df.rename(columns={'pay_0':'pay_1'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>limit_balance</th>\n",
       "      <th>sex</th>\n",
       "      <th>education_level</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>age</th>\n",
       "      <th>pay_1</th>\n",
       "      <th>pay_2</th>\n",
       "      <th>pay_3</th>\n",
       "      <th>pay_4</th>\n",
       "      <th>pay_5</th>\n",
       "      <th>...</th>\n",
       "      <th>bill_amt_4</th>\n",
       "      <th>bill_amt_5</th>\n",
       "      <th>bill_amt_6</th>\n",
       "      <th>pay_amt_1</th>\n",
       "      <th>pay_amt_2</th>\n",
       "      <th>pay_amt_3</th>\n",
       "      <th>pay_amt_4</th>\n",
       "      <th>pay_amt_5</th>\n",
       "      <th>pay_amt_6</th>\n",
       "      <th>def_payment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2806</th>\n",
       "      <td>200000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2815</th>\n",
       "      <td>200000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      limit_balance  sex  education_level  marital_status   age  pay_1  pay_2  \\\n",
       "2806       200000.0    2                1               1  34.0    1.0   -2.0   \n",
       "2815       200000.0    2                1               1  34.0    1.0   -2.0   \n",
       "\n",
       "      pay_3  pay_4  pay_5  ...  bill_amt_4  bill_amt_5  bill_amt_6  pay_amt_1  \\\n",
       "2806   -2.0   -2.0   -2.0  ...         0.0         0.0         0.0        0.0   \n",
       "2815   -2.0   -2.0   -2.0  ...         0.0         0.0         0.0        0.0   \n",
       "\n",
       "      pay_amt_2  pay_amt_3  pay_amt_4  pay_amt_5  pay_amt_6  def_payment  \n",
       "2806        0.0        0.0        0.0        0.0        0.0            0  \n",
       "2815        0.0        0.0        0.0        0.0        0.0            0  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mencheck duplikasi data \n",
    "df[df.duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#menremove index yang double\n",
    "index_to_remove = 2806\n",
    "df1 = df.drop(index_to_remove)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business Knowledge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#membuat barplot menampilkan presentase orang yang bisa membayar atau tidak\n",
    "count_data = df1['def_payment'].value_counts()\n",
    "\n",
    "total_data = len(df1)\n",
    "\n",
    "percentage_data = (count_data / total_data) * 100\n",
    "\n",
    "sns.barplot(x=percentage_data.index, y=percentage_data.values)\n",
    "\n",
    "plt.title('Presentasi Persentase Def Payment')\n",
    "plt.xlabel('def_payment')\n",
    "plt.ylabel('Persentase (%)')\n",
    "\n",
    "for i in range(len(percentage_data)):\n",
    "    plt.text(i, percentage_data[i], f'{percentage_data[i]:.2f}%', ha='center', va='bottom')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasil : Kita bisa melihat bahwa 78.58% bisa bisa membayar dan 21.42% tidak bisa membayar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def_count = (df1.sex.value_counts(normalize=True)*100)\n",
    "def_count.plot.bar(figsize=(6,6))\n",
    "plt.xticks(fontsize=12, rotation=0)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.title(\"Persentase dari Gender\", fontsize=15)\n",
    "for x,y in zip([0,1],def_count):\n",
    "    plt.text(x, y, '{:.2f}%'.format(y), fontsize=12)  # Memformat y menjadi dua angka desimal\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasil : Bisa dilihat bahwa terdapat 39.24% di laki-laki=1 dan 60.76% didominasi oleh perempuan=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 2)\n",
    "sns.histplot(df1['age'], kde=True)\n",
    "plt.title('Distribution of Age')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasil : Bisa dilihat bahwa persebaran umur dari umur 20 sampai 50\n",
    "Untuk Meningkatkan jumlah user yang bisa bayar ada baiknya untuk penagihan bisa dilakukan ditanggal gajian sekitar 25-31 pada saat gajian karena pada saat tanggal tersebut uang mereka jumlahnya banyak jadi bisa membayar "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cek Kolerasi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Menghitung kolerasi def_payment dengan 'pay_1', 'pay_2', 'pay_3', 'pay_4','pay_5','pay_6'\n",
    "#saya menggunakan kendall karena setelah dicek kembali bahwa datanya merupakan kategorical\n",
    "billcol = df1[['pay_1', 'pay_2', 'pay_3', 'pay_4','pay_5','pay_6','def_payment']]\n",
    "\n",
    "kendall_corr_matrix = billcol.corr(method=lambda x, y: kendalltau(x, y).correlation)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(kendall_corr_matrix, annot=True, cmap='coolwarm', center=0)\n",
    "plt.title('Kendall tau Correlation Heatmap Default Payment Method with Bill Payment')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasil : Dari heatmap bisa dilihat bahwa 'pay_1', 'pay_2', 'pay_3', 'pay_4','pay_5','pay_6' memiliki kolerasi dengan kolom def_payment sehingga kolom-kolom tersebut akan dimasukkan ke dalam feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Menghitung kolerasi def_payment dengan 'bill_amt_1', 'bill_amt_2', 'bill_amt_3', 'bill_amt_4','bill_amt_5','bill_amt_6'\n",
    "repaymentcol = df1[['bill_amt_1', 'bill_amt_2', 'bill_amt_3', 'bill_amt_4','bill_amt_5','bill_amt_6','def_payment']]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(repaymentcol.corr(), annot=True, cmap='coolwarm', center=0)\n",
    "plt.title('Correlation Heatmap Default Payment Method with Bill Payment')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasil : Dari heatmap bisa dilihat bahwa tidak ada kolom yang memiliki kolerasi dengan kolom def_payment sehingga kolom-kolom tersebut tidak akan dimasukkan ke dalam feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Menghitung kolerasi def_payment dengan 'pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4','pay_amt_5','pay_amt_5','limit_balance'\n",
    "pay_amt_corr = df1[['pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4','pay_amt_5','pay_amt_5','limit_balance','def_payment']]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(pay_amt_corr.corr(), annot=True, cmap='coolwarm', center=0)\n",
    "plt.title('Correlation Heatmap Default Payment Method with Bill Payment')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasil : Dari heatmap bisa dilihat bahwa 'limit_balance' memiliki kolerasi dengan kolom def_payment sehingga kolom-kolom tersebut akan dimasukkan ke dalam feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Menghitung kolerasi def_payment dengan 'sex', 'education_level', 'marital_status', 'age'\n",
    "#saya menggunakan kendall karena setelah dicek kembali bahwa datanya merupakan kategorical\n",
    "status_corr = df1[['sex', 'education_level', 'marital_status', 'age', 'def_payment']]\n",
    "\n",
    "kendall_corr_matrix = status_corr.corr(method=lambda x, y: kendalltau(x, y).correlation)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(kendall_corr_matrix, annot=True, cmap='coolwarm', center=0)\n",
    "plt.title('Kendall tau Correlation Heatmap Default Payment Method with Bill Payment')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasil : Dari heatmap bisa dilihat bahwa tidak ada kolom yang memiliki kolerasi dengan kolom def_payment sehingga kolom-kolom tersebut tidak akan dimasukkan ke dalam feature selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#membuat variabel baru sebgai feature selection\n",
    "df_fix = df1[['limit_balance','pay_1', 'pay_2', 'pay_3', 'pay_4','pay_5','pay_6','def_payment']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>limit_balance</th>\n",
       "      <th>pay_1</th>\n",
       "      <th>pay_2</th>\n",
       "      <th>pay_3</th>\n",
       "      <th>pay_4</th>\n",
       "      <th>pay_5</th>\n",
       "      <th>pay_6</th>\n",
       "      <th>def_payment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>260000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
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       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2963</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2964</th>\n",
       "      <td>290000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2964 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      limit_balance  pay_1  pay_2  pay_3  pay_4  pay_5  pay_6  def_payment\n",
       "0           80000.0    0.0    0.0    0.0    0.0    0.0    0.0            1\n",
       "1          200000.0    0.0    0.0    0.0    0.0    0.0    0.0            0\n",
       "2           20000.0    0.0    0.0    0.0    0.0    0.0    0.0            1\n",
       "3          260000.0    0.0    0.0    0.0    0.0    0.0    0.0            0\n",
       "4          150000.0    0.0    0.0    0.0   -1.0    0.0    0.0            0\n",
       "...             ...    ...    ...    ...    ...    ...    ...          ...\n",
       "2960        80000.0   -1.0   -1.0   -1.0   -2.0   -2.0   -2.0            0\n",
       "2961        50000.0   -1.0   -1.0   -1.0   -1.0   -2.0   -2.0            1\n",
       "2962       450000.0   -2.0   -2.0   -2.0   -2.0   -2.0   -2.0            1\n",
       "2963        50000.0   -2.0   -2.0   -2.0   -2.0   -2.0   -2.0            0\n",
       "2964       290000.0    1.0   -2.0   -2.0   -2.0   -2.0   -2.0            0\n",
       "\n",
       "[2964 rows x 8 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#memanggil data yang sudah di features selection\n",
    "df_fix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cardinality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cardinality kolom pay_1: 11\n",
      "cardinality kolom pay_2: 10\n",
      "cardinality kolom pay_3: 9\n",
      "cardinality kolom pay_4: 10\n",
      "cardinality kolom pay_5: 9\n",
      "cardinality kolom pay_6: 8\n"
     ]
    }
   ],
   "source": [
    "#menghitung cardinality\n",
    "cols_cat= df_fix[['pay_1','pay_2','pay_3','pay_4','pay_5','pay_6']]\n",
    "\n",
    "for i in cols_cat.columns:\n",
    "    print(f'cardinality kolom {i}:', cols_cat[i].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasil : Setelah dicek bahwa cardinality pada kolom 'pay_1','pay_2','pay_3','pay_4','pay_5','pay_6' paling tinggi 11 dan masih dalam batas normal menurut saya sehingga tidak perlu di handling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# vi. Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#membagi data yang sudah di future selection menjadi x dan y dengan y yaitu kolom target def_payment\n",
    "X= df_fix.drop('def_payment', axis=1) #fitur\n",
    "y= df_fix['def_payment'] #target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Size :  (2371, 7)\n",
      "Test Size  :  (593, 7)\n",
      "Train Size :  (2371,)\n",
      "Test Size  :  (593,)\n"
     ]
    }
   ],
   "source": [
    "#membagi data menjadi X_train, X_test, y_train, y_test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=3,stratify=y)\n",
    "\n",
    "print('Train Size : ', X_train.shape)\n",
    "print('Test Size  : ', X_test.shape)\n",
    "print('Train Size : ', y_train.shape)\n",
    "print('Test Size  : ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness dari distance :0.9529337693438512\n",
      "Skewness dari distance :1.1988766501981816\n"
     ]
    }
   ],
   "source": [
    "#menghitung persebaran jenis data dengan metode skewness\n",
    "print(f\"Skewness dari distance :{X_train['limit_balance'].skew()}\")\n",
    "print(f\"Skewness dari distance :{X_test['limit_balance'].skew()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#menghapus outlier pada kolom limit_balance di X train dan X test dengan tukey karena x test skewnessnya lebih dari 1 dan x train skewnessnya mendekati 1 dan outlier kurangd air 5%\n",
    "Trimmer_price = OutlierTrimmer(capping_method='iqr',\n",
    "                             tail='both',\n",
    "                             fold=1.5,\n",
    "                             variables=['limit_balance'])\n",
    "\n",
    "X_train_trimmer = Trimmer_price.fit_transform(X_train)\n",
    "X_test_trimmer = Trimmer_price.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "limit_balance    0\n",
       "pay_1            0\n",
       "pay_2            0\n",
       "pay_3            0\n",
       "pay_4            0\n",
       "pay_5            0\n",
       "pay_6            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ketika dicek tidak ada missing value pada x train\n",
    "X_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "limit_balance    0\n",
       "pay_1            0\n",
       "pay_2            0\n",
       "pay_3            0\n",
       "pay_4            0\n",
       "pay_5            0\n",
       "pay_6            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ketika dicek tidak ada missing value pada x test\n",
    "X_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#melakukan scalling dengan menggunakan standard scaller\n",
    "scaler = MinMaxScaler()\n",
    "model = scaler.fit(X_train_trimmer[['limit_balance']])\n",
    "train_scaller = model.transform(X_train_trimmer[['limit_balance']])\n",
    "test_scaller = scaler.transform(X_test_trimmer[['limit_balance']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.58],\n",
       "       [0.5 ],\n",
       "       [0.56],\n",
       "       ...,\n",
       "       [0.24],\n",
       "       [0.14],\n",
       "       [0.24]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#memanggil kolom limit balanace di train yang sudah di scaling\n",
    "train_scaller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
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       "       [0.48]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#memanggil kolom limit balanace di test yang sudah di scaling\n",
    "test_scaller"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Menggabungkan data yang sudah di data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#membuat variabel baru untuk menampung kolom kategorical pada x train dan x test\n",
    "X_train_cat=X_train_trimmer[['pay_1', 'pay_2', 'pay_3', 'pay_4','pay_5','pay_6']]\n",
    "X_test_cat=X_test_trimmer[['pay_1', 'pay_2', 'pay_3', 'pay_4','pay_5','pay_6']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#menggabungkan hasil dari x train. dan x test numerical dan x train kategorical yang sudah di data preprocessing\n",
    "X_train_final = np.concatenate([train_scaller, X_train_cat], axis=1)\n",
    "X_test_final = np.concatenate([test_scaller, X_test_cat], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.58,  0.  ,  0.  , ...,  0.  ,  0.  ,  2.  ],\n",
       "       [ 0.5 ,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       [ 0.56,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       ...,\n",
       "       [ 0.24,  0.  ,  0.  , ...,  0.  , -2.  , -2.  ],\n",
       "       [ 0.14,  2.  ,  2.  , ...,  2.  ,  0.  ,  0.  ],\n",
       "       [ 0.24, -1.  ,  2.  , ...,  2.  , -1.  , -1.  ]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#memanggil x train final yang sudah di gabung\n",
    "X_train_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.12,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       [ 0.08,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       [ 0.4 ,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       ...,\n",
       "       [ 0.36,  0.  ,  0.  , ...,  0.  ,  0.  , -1.  ],\n",
       "       [ 0.08,  0.  ,  0.  , ...,  0.  ,  2.  ,  0.  ],\n",
       "       [ 0.48,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#memanggil x test final yang sudah di gabung\n",
    "X_test_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#menyamakan index pada y train dan y test dengan fungsi loc\n",
    "y_train = y_train.loc[X_train_trimmer.index]\n",
    "y_test = y_test.loc[X_test_trimmer.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.58,  0.  ,  0.  , ...,  0.  ,  0.  ,  2.  ],\n",
       "       [ 0.5 ,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       [ 0.56,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       ...,\n",
       "       [ 0.24,  0.  ,  0.  , ...,  0.  , -2.  , -2.  ],\n",
       "       [ 0.14,  2.  ,  2.  , ...,  2.  ,  0.  ,  0.  ],\n",
       "       [ 0.24, -1.  ,  2.  , ...,  2.  , -1.  , -1.  ]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SmotheNC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1855\n",
       "1    1855\n",
       "Name: def_payment, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#melakukan smotenc untuk membalancekan data\n",
    "smotenc = SMOTENC([1,2,3,4,5,6], random_state = 66)\n",
    "x_train_balanced, y_train_balanced = smotenc.fit_resample(X_train_final, y_train)\n",
    "y_train_balanced.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#membuat varibael baru data yang sudah di balance\n",
    "X_train_finals= x_train_balanced\n",
    "y_trains= y_train_balanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# vii Model Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient :  [[-0.90840624  0.61806286 -0.07365562  0.15539639 -0.07593628  0.04412125\n",
      "   0.12469467]]\n",
      "Intercept   :  [0.10615282]\n"
     ]
    }
   ],
   "source": [
    "#mendefine model Logistic Refression\n",
    "model_logreg = LogisticRegression()\n",
    "model_logreg.fit(X_train_finals, y_trains)\n",
    "\n",
    "print('Coefficient : ', model_logreg.coef_)\n",
    "print('Intercept   : ', model_logreg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------default Logistic Regression-------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.72      0.69      1855\n",
      "           1       0.70      0.65      0.67      1855\n",
      "\n",
      "    accuracy                           0.68      3710\n",
      "   macro avg       0.68      0.68      0.68      3710\n",
      "weighted avg       0.68      0.68      0.68      3710\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#melihat nilai default model logistic regression dari data x train\n",
    "y_train_predict_logreg = model_logreg.predict(X_train_finals)\n",
    "print('----------default Logistic Regression-------------')\n",
    "print(classification_report(y_trains,y_train_predict_logreg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------default Logistic Regression-------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.72      0.79       464\n",
      "           1       0.38      0.61      0.47       127\n",
      "\n",
      "    accuracy                           0.70       591\n",
      "   macro avg       0.62      0.67      0.63       591\n",
      "weighted avg       0.77      0.70      0.72       591\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#melihat nilai default model logistic regression dari data x test\n",
    "y_test_predict_logreg = model_logreg.predict(X_test_final)\n",
    "print('----------default Logistic Regression-------------')\n",
    "print(classification_report(y_test,y_test_predict_logreg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dilihat dari nilai defult Logistic Regression f1_score memiliki nilai range train 0.67 dan test 0.47 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mendefine model KNN\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train_finals, y_trains)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------default KNN-------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.88      0.82      1855\n",
      "           1       0.86      0.72      0.79      1855\n",
      "\n",
      "    accuracy                           0.80      3710\n",
      "   macro avg       0.81      0.80      0.80      3710\n",
      "weighted avg       0.81      0.80      0.80      3710\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#melihat nilai default model KNN dari data x train\n",
    "y_train_predict_knn = knn.predict(X_train_finals)\n",
    "print('----------default KNN-------------')\n",
    "print(classification_report(y_trains,y_train_predict_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------default KNN-------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.85      0.85       464\n",
      "           1       0.47      0.49      0.48       127\n",
      "\n",
      "    accuracy                           0.77       591\n",
      "   macro avg       0.66      0.67      0.67       591\n",
      "weighted avg       0.77      0.77      0.77       591\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#melihat nilai default model KNN dari data x test\n",
    "y_test_predict_knn = knn.predict(X_test_final)\n",
    "print('----------default KNN-------------')\n",
    "print(classification_report(y_test,y_test_predict_knn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dilihat dari nilai defult KNN f1_score memiliki nilai range train 0.79 dan test 0.48"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mendefine model SVM\n",
    "svm_scaled = SVC()\n",
    "svm_scaled.fit(X_train_finals, y_trains)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------default SVM-------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.89      0.76      1855\n",
      "           1       0.84      0.56      0.67      1855\n",
      "\n",
      "    accuracy                           0.73      3710\n",
      "   macro avg       0.75      0.73      0.72      3710\n",
      "weighted avg       0.75      0.73      0.72      3710\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#melihat nilai default model SVM dari data x train\n",
    "y_train_predict_svm = svm_scaled.predict(X_train_finals)\n",
    "print('----------default SVM-------------')\n",
    "print(classification_report(y_trains,y_train_predict_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------default SVM-------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.88      0.88       464\n",
      "           1       0.55      0.52      0.53       127\n",
      "\n",
      "    accuracy                           0.80       591\n",
      "   macro avg       0.71      0.70      0.70       591\n",
      "weighted avg       0.80      0.80      0.80       591\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#melihat nilai default model SVM dari data x test\n",
    "y_test_predict_svm = svm_scaled.predict(X_test_final)\n",
    "print('----------default SVM-------------')\n",
    "print(classification_report(y_test,y_test_predict_svm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dilihat dari nilai defult SVM f1_score memiliki nilai range train 0.67 dan test 0.53"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Disini saya melelakukan cross validation pada 3 model diantara Logistic Regression, KNN dan SVM. Dari hasil cross validation model terbaik akan diambil untuk dilakukan hypermeterparatuning dan ditraining serta di evaluasi. Saya menggunakan penilaian F1 score karena saya menganggap precision dan recall penting "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cross validation dengan metode logistec regession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - All - Cross Validation  :  [0.68938401 0.62857143 0.66199158 0.697609   0.68775791]\n",
      "F1 Score - Mean - Cross Validation :  0.6730627869063035\n",
      "F1 Score - Std - Cross Validation  :  0.02524637830058187\n",
      "F1 Score - Range of Test-Set       :  0.6478164086057216 - 0.6983091652068854\n",
      "CPU times: total: 406 ms\n",
      "Wall time: 130 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "f1_train_cross_val = cross_val_score(model_logreg,\n",
    "                                     X_train_finals,\n",
    "                                     y_trains,\n",
    "                                     cv=5,\n",
    "                                     scoring=\"f1\")\n",
    "\n",
    "print('F1 Score - All - Cross Validation  : ', f1_train_cross_val)\n",
    "print('F1 Score - Mean - Cross Validation : ', f1_train_cross_val.mean())\n",
    "print('F1 Score - Std - Cross Validation  : ', f1_train_cross_val.std())\n",
    "print('F1 Score - Range of Test-Set       : ', (f1_train_cross_val.mean()-f1_train_cross_val.std()) , '-', (f1_train_cross_val.mean()+f1_train_cross_val.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dilihat dari nilai STD dari model Logistic Regression 0.025 bisa dibilang bahwa model ini best fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cross validation dengan metode knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - All - Cross Validation  :  [0.68085106 0.69822485 0.7532097  0.75177305 0.74532374]\n",
      "F1 Score - Mean - Cross Validation :  0.7258764813962675\n",
      "F1 Score - Std - Cross Validation  :  0.030291364602699242\n",
      "F1 Score - Range of Test-Set       :  0.6955851167935683 - 0.7561678459989667\n",
      "CPU times: total: 812 ms\n",
      "Wall time: 272 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "f1_train_cross_val = cross_val_score(knn,\n",
    "                                     X_train_finals,\n",
    "                                     y_trains,\n",
    "                                     cv=5,\n",
    "                                     scoring=\"f1\")\n",
    "\n",
    "print('F1 Score - All - Cross Validation  : ', f1_train_cross_val)\n",
    "print('F1 Score - Mean - Cross Validation : ', f1_train_cross_val.mean())\n",
    "print('F1 Score - Std - Cross Validation  : ', f1_train_cross_val.std())\n",
    "print('F1 Score - Range of Test-Set       : ', (f1_train_cross_val.mean()-f1_train_cross_val.std()) , '-', (f1_train_cross_val.mean()+f1_train_cross_val.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dilihat dari nilai STD dari model KNN 0.03 bisa dibilang bahwa model ini best fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cross validation dengan metode SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - All - Cross Validation  :  [0.66242038 0.60883797 0.63578275 0.68330733 0.6793049 ]\n",
      "F1 Score - Mean - Cross Validation :  0.653930665983441\n",
      "F1 Score - Std - Cross Validation  :  0.028077085118753956\n",
      "F1 Score - Range of Test-Set       :  0.625853580864687 - 0.6820077511021949\n",
      "CPU times: total: 2.92 s\n",
      "Wall time: 2.93 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "f1_train_cross_val = cross_val_score(svm_scaled,\n",
    "                                     X_train_finals,\n",
    "                                     y_trains,\n",
    "                                     cv=5,\n",
    "                                     scoring=\"f1\")\n",
    "\n",
    "print('F1 Score - All - Cross Validation  : ', f1_train_cross_val)\n",
    "print('F1 Score - Mean - Cross Validation : ', f1_train_cross_val.mean())\n",
    "print('F1 Score - Std - Cross Validation  : ', f1_train_cross_val.std())\n",
    "print('F1 Score - Range of Test-Set       : ', (f1_train_cross_val.mean()-f1_train_cross_val.std()) , '-', (f1_train_cross_val.mean()+f1_train_cross_val.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dilihat dari nilai STD dari model SVM 0.025 bisa dibilang bahwa model ini best fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kesimpulan : dari hasil cross validation diatas saya mengambil model KNN untuk di hypertunning parameter karena hasilnya paling bagus diantara 3 model yang lain dengan nilai F1 Score - Range of Test-Set 0.7048053351122697 - 0.7562437364204834"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# viii. Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mendefine parameter untuk metode SVM \n",
    "#Saya memakai 2 parameter saja untuk mempersingkat waktu\n",
    "random_search_params = {\n",
    "    'weights': ['uniform', 'distance'],\n",
    "    'p': [1, 2]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\USER\\miniconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:307: UserWarning: The total space of parameters 4 is smaller than n_iter=100. Running 4 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=KNeighborsClassifier(), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;p&#x27;: [1, 2],\n",
       "                                        &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
       "                   random_state=3, scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=KNeighborsClassifier(), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;p&#x27;: [1, 2],\n",
       "                                        &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
       "                   random_state=3, scoring=&#x27;f1&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=KNeighborsClassifier(), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'p': [1, 2],\n",
       "                                        'weights': ['uniform', 'distance']},\n",
       "                   random_state=3, scoring='f1')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#melakukan hyperparameter tunning dengan metode random search\n",
    "rf_randomcv = RandomizedSearchCV(estimator=KNeighborsClassifier(),\n",
    "                                 param_distributions=random_search_params,\n",
    "                                 n_iter=100,\n",
    "                                 cv=5,\n",
    "                                 random_state=3,\n",
    "                                 n_jobs=-1,\n",
    "                                 scoring='f1')\n",
    "rf_randomcv.fit(X_train_finals, y_trains)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'weights': 'distance', 'p': 2}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#memanggil parameter terbaik dalam random cv \n",
    "rf_randomcv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_randomcv_best = rf_randomcv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7340693823888363\n",
      "0.007639635271124033\n"
     ]
    }
   ],
   "source": [
    "#melihat mean dan standard deviasi dari hyperparamter tuning random search\n",
    "print(rf_randomcv.cv_results_['mean_test_score'].mean())\n",
    "print(rf_randomcv.cv_results_['mean_test_score'].std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasil : dari hasil std yang mendekati 0 dan berada di bawah mean berarti model ini adalah BEST FIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - Train Set  :  0.8894767783656672 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.98      0.91      1855\n",
      "           1       0.98      0.82      0.89      1855\n",
      "\n",
      "    accuracy                           0.90      3710\n",
      "   macro avg       0.91      0.90      0.90      3710\n",
      "weighted avg       0.91      0.90      0.90      3710\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002052A576100>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#melihat hasil data yang sudah di training dengan metode random search\n",
    "y_pred_train_finals = rf_randomcv_best.predict(X_train_finals)\n",
    "\n",
    "print('F1 Score - Train Set  : ', f1_score(y_trains, y_pred_train_finals), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_trains, y_pred_train_finals), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(rf_randomcv, X_train_finals, y_trains, cmap='Reds'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - Train Set  :  0.46025104602510464 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.88      0.86       464\n",
      "           1       0.49      0.43      0.46       127\n",
      "\n",
      "    accuracy                           0.78       591\n",
      "   macro avg       0.67      0.66      0.66       591\n",
      "weighted avg       0.77      0.78      0.78       591\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002052B646E20>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#menampilkan hasil data test dengan metode random search\n",
    "y_pred_test_finals = rf_randomcv_best.predict(X_test_final)\n",
    "\n",
    "print('F1 Score - Train Set  : ', f1_score(y_test, y_pred_test_finals), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_test, y_pred_test_finals), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(rf_randomcv, X_test_final, y_test, cmap='Reds'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "kesimpulan : dilihat dari nilai defult KNN f1_score random search memiliki nilai range train 0.89 dan test 0.46"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ix Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>Baseline (Default Hyperparameter)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test - accuracy_score</th>\n",
       "      <td>0.771574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test - f1_score</th>\n",
       "      <td>0.478764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test - precision</th>\n",
       "      <td>0.469697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test - recall</th>\n",
       "      <td>0.488189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train - accuracy</th>\n",
       "      <td>0.802965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train - f1_score</th>\n",
       "      <td>0.785568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train - precision</th>\n",
       "      <td>0.861647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train - recall</th>\n",
       "      <td>0.721833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Baseline (Default Hyperparameter)\n",
       "test - accuracy_score                           0.771574\n",
       "test - f1_score                                 0.478764\n",
       "test - precision                                0.469697\n",
       "test - recall                                   0.488189\n",
       "train - accuracy                                0.802965\n",
       "train - f1_score                                0.785568\n",
       "train - precision                               0.861647\n",
       "train - recall                                  0.721833"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#membuat table baru default untuk nantinya membandingkan dengan nilai f1 score test yang default dan yang sudah dengan menggunakan random seach\n",
    "all_reports = {}\n",
    "def performance_report(all_reports, y_trains, y_train_predict_knn, y_test, y_test_predict_knn, name):\n",
    "  score_reports = {\n",
    "      'train - precision' : precision_score(y_trains, y_train_predict_knn),\n",
    "      'train - recall' : recall_score(y_trains, y_train_predict_knn),\n",
    "      'train - accuracy' : accuracy_score(y_trains, y_train_predict_knn),\n",
    "      'train - f1_score' : f1_score(y_trains, y_train_predict_knn),\n",
    "      'test - precision' : precision_score(y_test, y_test_predict_knn),\n",
    "      'test - recall' : recall_score(y_test, y_test_predict_knn),\n",
    "      'test - accuracy_score' : accuracy_score(y_test, y_test_predict_knn),\n",
    "      'test - f1_score' : f1_score(y_test, y_test_predict_knn),\n",
    "  }\n",
    "  all_reports[name] = score_reports\n",
    "  return all_reports\n",
    "\n",
    "all_reports = performance_report(all_reports, y_trains, y_train_predict_knn, y_test, y_test_predict_knn, 'Baseline (Default Hyperparameter)')\n",
    "pd.DataFrame(all_reports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>Baseline (Default Hyperparameter)</th>\n",
       "      <th>Random Search</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train - precision</th>\n",
       "      <td>0.861647</td>\n",
       "      <td>0.978022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train - recall</th>\n",
       "      <td>0.721833</td>\n",
       "      <td>0.815633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train - accuracy</th>\n",
       "      <td>0.802965</td>\n",
       "      <td>0.898652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train - f1_score</th>\n",
       "      <td>0.785568</td>\n",
       "      <td>0.889477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test - precision</th>\n",
       "      <td>0.469697</td>\n",
       "      <td>0.491071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test - recall</th>\n",
       "      <td>0.488189</td>\n",
       "      <td>0.433071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test - accuracy_score</th>\n",
       "      <td>0.771574</td>\n",
       "      <td>0.781726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test - f1_score</th>\n",
       "      <td>0.478764</td>\n",
       "      <td>0.460251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Baseline (Default Hyperparameter)  Random Search\n",
       "train - precision                               0.861647       0.978022\n",
       "train - recall                                  0.721833       0.815633\n",
       "train - accuracy                                0.802965       0.898652\n",
       "train - f1_score                                0.785568       0.889477\n",
       "test - precision                                0.469697       0.491071\n",
       "test - recall                                   0.488189       0.433071\n",
       "test - accuracy_score                           0.771574       0.781726\n",
       "test - f1_score                                 0.478764       0.460251"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#menampilkan tabel perbandingan nilai dari f1 test yang default dan dengan metode random search\n",
    "all_reports = performance_report(all_reports, y_trains, y_pred_train_finals, y_test, y_pred_test_finals, 'Random Search')\n",
    "pd.DataFrame(all_reports)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hadil : Dari Hasil kesimpulan bisa didapatkan bahwa untuk test-f1score dari model KNN yang lebih bagus adalah yang default. Jadi model yang akan dipakai pada data inference untuk mempredict apakah user berhasil bayar atau tidak adalah KNN default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mendefine fungsi KNN yang sudah di hyperparameter tuning dengan fungsi random search untuk di simpan dalam file pkl\n",
    "model_knn=KNeighborsClassifier().fit(X_train_finals,y_trains)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# x. Model Saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Saving\n",
    "\n",
    "with open('scaler.pkl', 'wb') as file_1:\n",
    "  pickle.dump(scaler, file_1)\n",
    "\n",
    "with open('model_knn.pkl', 'wb') as file_2:\n",
    "  pickle.dump(model_knn, file_2)"
   ]
  }
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