main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 0.9990\n",
      "Random Forest Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1939\n",
      "           1       1.00      0.97      0.98        61\n",
      "\n",
      "    accuracy                           1.00      2000\n",
      "   macro avg       1.00      0.98      0.99      2000\n",
      "weighted avg       1.00      1.00      1.00      2000\n",
      "\n",
      "Random Forest Confusion Matrix:\n",
      "[[1939    0]\n",
      " [   2   59]]\n",
      "\n",
      "Random Forest ROC AUC Score: 0.9816\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mengimport library\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Men-Load dataset dan menyiapkan data\n",
    "name = 'data.csv'\n",
    "data = pd.read_csv(name)\n",
    "\n",
    "data = data.dropna()\n",
    "data = pd.get_dummies(data, columns=['Type', 'Product ID'])\n",
    "\n",
    "X = data.drop(['UID', 'Machine failure'], axis=1)\n",
    "y = data['Machine failure']\n",
    "\n",
    "# Membagi dataset menjadi set train dan test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Men-Train random forest model\n",
    "rf_model = RandomForestClassifier(random_state=33)\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# Membuat prediksi\n",
    "y_pred = rf_model.predict(X_test)\n",
    "y_proba = rf_model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Menghitung hasil\n",
    "accuracy = rf_model.score(X_test, y_test)\n",
    "report = classification_report(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "roc_auc = roc_auc_score(y_test, y_proba)\n",
    "\n",
    "# Kesimpulan\n",
    "print(f\"Random Forest Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Random Forest Classification Report:\")\n",
    "print(report)\n",
    "print(f\"Random Forest Confusion Matrix:\")\n",
    "print(conf_matrix)\n",
    "print(f\"\\nRandom Forest ROC AUC Score: {roc_auc:.4f}\")\n",
    "\n",
    "# Plot kurva ROC\n",
    "fpr, tpr, _ = roc_curve(y_test, y_proba)\n",
    "plt.plot(fpr, tpr, marker='.')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Kurva ROC')\n",
    "plt.show()\n"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}