{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import library\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import json\n",
    "import warnings\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder  # Assuming these scalers/encoders are needed\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.pipeline import make_pipeline, Pipeline\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('best_svm_model.pkl', 'rb') as file_1:\n",
    "  list_cat_cols = pickle.load(file_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data Yang Sudah Dibuat Untuk Random State Data Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>...</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",
       "      <th>Klasifikasi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>240000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11756.0</td>\n",
       "      <td>12522.0</td>\n",
       "      <td>40529.0</td>\n",
       "      <td>3211.0</td>\n",
       "      <td>9795.0</td>\n",
       "      <td>11756.0</td>\n",
       "      <td>12522.0</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Dewasa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  limit_balance  sex  education_level  marital_status   age  \\\n",
       "0           0       240000.0    2                2               1  41.0   \n",
       "\n",
       "   pay_1  pay_2  pay_3  pay_4  ...  bill_amt_5  bill_amt_6  pay_amt_1  \\\n",
       "0    1.0   -1.0   -1.0   -1.0  ...     11756.0     12522.0    40529.0   \n",
       "\n",
       "   pay_amt_2  pay_amt_3  pay_amt_4  pay_amt_5  pay_amt_6  \\\n",
       "0     3211.0     9795.0    11756.0    12522.0     6199.0   \n",
       "\n",
       "   default_payment_next_month  Klasifikasi  \n",
       "0                           0       Dewasa  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create new data\n",
    "data_inf = pd.read_csv('data_inf')\n",
    "data_inf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Load Data Yang Sudah Dibuat Untuk Random State Data Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default_prediction: 0\n"
     ]
    }
   ],
   "source": [
    "y_pred_inf = list_cat_cols.predict(data_inf)\n",
    "\n",
    "print('default_prediction:', (y_pred_inf[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kesimpulan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari hasil inferenece dengan nilai 0 Jika di tarik kesimpulan dan ditinjau pada studi kasus dalam memprediksi `default_payment_next_month` maka dapat dikatakan bahwa `tidak ada kemungkinan` terjadi telat pembayaran di bulan berikutnya  "
   ]
  }
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