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P1G5_Set_1_badriah_nursakinah_inference.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import Library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": 6,
+   "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": 7,
+   "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": 7,
+     "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": 8,
+   "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  "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

data_inf

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+,limit_balance,sex,education_level,marital_status,age,pay_1,pay_2,pay_3,pay_4,pay_5,pay_6,bill_amt_1,bill_amt_2,bill_amt_3,bill_amt_4,bill_amt_5,bill_amt_6,pay_amt_1,pay_amt_2,pay_amt_3,pay_amt_4,pay_amt_5,pay_amt_6,default_payment_next_month,Klasifikasi
+0,240000.0,2,2,1,41.0,1.0,-1.0,-1.0,-1.0,-1,-1,0.0,40529.0,3211.0,9795.0,11756.0,12522.0,40529.0,3211.0,9795.0,11756.0,12522.0,6199.0,0,Dewasa