File size: 5,857 Bytes
8247efc e67ac61 8247efc e67ac61 8247efc e67ac61 8247efc e67ac61 8247efc e67ac61 8247efc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 |
{
"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 "
]
}
],
"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
}
|