In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
\n",
+ "
\n",
+ " \n",
+ " Parameters\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
criterion
\n",
+ "
'gini'
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
splitter
\n",
+ "
'best'
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
max_depth
\n",
+ "
None
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
min_samples_split
\n",
+ "
2
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
min_samples_leaf
\n",
+ "
1
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
min_weight_fraction_leaf
\n",
+ "
0.0
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
max_features
\n",
+ "
None
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
random_state
\n",
+ "
42
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
max_leaf_nodes
\n",
+ "
None
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
min_impurity_decrease
\n",
+ "
0.0
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
class_weight
\n",
+ "
None
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
ccp_alpha
\n",
+ "
0.0
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
monotonic_cst
\n",
+ "
None
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "DecisionTreeClassifier(random_state=42)"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# TODO: Train the model using training data\n",
+ "model.fit(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_jPiQ1nNc1Ob"
+ },
+ "source": [
+ "### TODO #2 :\n",
+ "\n",
+ "Isi insight, kenapa menggunakan Decision Tree? Apakah ada algoritma lain yang serupa?\n",
+ "\n",
+ "**Insight:**\n",
+ "**Alasan menggunakan Decision Tree:**\n",
+ "1. **Interpretability**: Decision Tree mudah dipahami dan divisualisasikan, kita bisa melihat path keputusan dengan jelas\n",
+ "2. **No preprocessing**: Tidak memerlukan normalisasi atau scaling data\n",
+ "3. **Handle mixed data types**: Bisa menangani data numerik dan kategorikal\n",
+ "4. **Feature importance**: Memberikan informasi fitur mana yang paling penting untuk klasifikasi\n",
+ "5. **Non-linear relationships**: Bisa menangani hubungan non-linear antar fitur\n",
+ "\n",
+ "**Algoritma serupa:**\n",
+ "- **Random Forest**: Ensemble dari banyak decision trees\n",
+ "- **Gradient Boosting**: XGBoost, LightGBM, CatBoost\n",
+ "- **Extra Trees**: Extremely Randomized Trees\n",
+ "- **AdaBoost**: Adaptive Boosting dengan decision trees sebagai base learner\n",
+ "\n",
+ "Untuk dataset Iris yang relatif sederhana, Decision Tree tunggal sudah cukup efektif dan memberikan hasil yang mudah diinterpretasi."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZyFz_NvD3NnG"
+ },
+ "source": [
+ "### 7. Evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "id": "yaOMbQCH3N1A"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 1.0\n",
+ "\n",
+ "Confusion Matrix:\n",
+ " [[10 0 0]\n",
+ " [ 0 9 0]\n",
+ " [ 0 0 11]]\n",
+ "\n",
+ "Classification Report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 10\n",
+ " 1 1.00 1.00 1.00 9\n",
+ " 2 1.00 1.00 1.00 11\n",
+ "\n",
+ " accuracy 1.00 30\n",
+ " macro avg 1.00 1.00 1.00 30\n",
+ "weighted avg 1.00 1.00 1.00 30\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# TODO: Evaluate model performance\n",
+ "y_pred = model.predict(X_test)\n",
+ "\n",
+ "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
+ "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
+ "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-vLv7xi7dAoQ"
+ },
+ "source": [
+ "### TODO #3 :\n",
+ "\n",
+ "Isi insight, analisis hasil *confusion matrix* ini, apakah ini disebut sebagai overfitting?\n",
+ "\n",
+ "**Insight:**\n",
+ "**Analisis Confusion Matrix:**\n",
+ "```\n",
+ "[[10 0 0] <- Setosa: 10 benar, 0 salah\n",
+ " [ 0 9 0] <- Versicolor: 9 benar, 0 salah \n",
+ " [ 0 0 11]] <- Virginica: 11 benar, 0 salah\n",
+ "```\n",
+ "\n",
+ "**Hasil menunjukkan:**\n",
+ "- **Accuracy 100%**: Model memprediksi semua data test dengan benar\n",
+ "- **Perfect Classification**: Tidak ada kesalahan klasifikasi (off-diagonal = 0)\n",
+ "- **Precision, Recall, F1-score = 1.00** untuk semua kelas\n",
+ "\n",
+ "**Apakah ini overfitting?**\n",
+ "**Kemungkinan besar YA, ini indikasi overfitting** karena:\n",
+ "1. **Perfect score suspicious**: Akurasi 100% pada real-world data jarang terjadi\n",
+ "2. **Dataset Iris terlalu mudah**: Iris adalah dataset yang sangat well-separated\n",
+ "3. **Decision Tree prone to overfitting**: Cenderung menghafal training data\n",
+ "4. **No regularization**: Tidak ada parameter kontrol kompleksitas model\n",
+ "\n",
+ "**Solusi**: Gunakan cross-validation, set max_depth, min_samples_split, atau coba dengan dataset yang lebih challenging."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cgqBBmkN5UCM"
+ },
+ "source": [
+ "### 8. Visualization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "id": "IrrOlDHk5UPC"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# TODO: Visualize the Decision Tree\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plot_tree(model, feature_names=iris.feature_names, class_names=iris.target_names, filled=True)\n",
+ "plt.title(\"Decision Tree for Iris Classification\")\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YFDXDsP7dh0Y"
+ },
+ "source": [
+ "### TODO #4 :\n",
+ "\n",
+ "Isi insight, berikan penjelasan cara kerja dari Decision Tree berdasarkan output diatas.\n",
+ "\n",
+ "**Insight:**\n",
+ "**Cara Kerja Decision Tree untuk Iris Classification:**\n",
+ "\n",
+ "**1. Root Node (Akar):**\n",
+ "- Dimulai dengan **petal length ≤ 2.45 cm**\n",
+ "- Ini adalah fitur paling penting untuk membedakan spesies\n",
+ "- Gini = 0.667 menunjukkan tingkat impurity awal\n",
+ "\n",
+ "**2. Level 1 - Pemisahan Pertama:**\n",
+ "- **Kiri (True)**: Jika petal length ≤ 2.45 → **Setosa** (40 samples, gini=0.0)\n",
+ "- **Kanan (False)**: Jika petal length > 2.45 → Perlu pemisahan lebih lanjut\n",
+ "\n",
+ "**3. Level 2 - Pemisahan Kedua:**\n",
+ "- Untuk yang bukan Setosa, gunakan **petal width ≤ 1.75 cm**\n",
+ "- **Kiri**: Mayoritas **Versicolor** \n",
+ "- **Kanan**: Mayoritas **Virginica**\n",
+ "\n",
+ "**4. Level Selanjutnya:**\n",
+ "- Menggunakan kombinasi **petal length**, **petal width**, dan **sepal width**\n",
+ "- Terus memisahkan sampai mencapai leaf nodes dengan gini=0.0 (pure)\n",
+ "\n",
+ "**Key Insights:**\n",
+ "- **Petal features lebih penting** daripada sepal untuk klasifikasi Iris\n",
+ "- **Setosa mudah dipisahkan** hanya dengan 1 aturan sederhana\n",
+ "- **Versicolor dan Virginica** memerlukan beberapa aturan kombinasi\n",
+ "- Tree mencapai **perfect purity** di semua leaf nodes (gini=0.0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KIiKbpr05dBq"
+ },
+ "source": [
+ "### 9. Prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "id": "82TqR8TA5djc"
+ },
+ "outputs": [],
+ "source": [
+ "# predict new data\n",
+ "def predict_iris(sample):\n",
+ " pred_class = model.predict([sample])[0]\n",
+ " return iris.target_names[pred_class]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "=== PERCOBAAN PREDIKSI IRIS ===\n",
+ "\n",
+ "Test 1: [5.0, 3.5, 1.5, 0.2]\n",
+ "Prediksi: setosa\n",
+ "Alasan: Petal length = 1.5 cm (< 2.45) → Setosa\n",
+ "\n",
+ "Test 2: [6.0, 3.0, 4.0, 1.3]\n",
+ "Prediksi: versicolor\n",
+ "Alasan: Petal length = 4.0 cm (> 2.45) & petal width = 1.3 cm (≤ 1.75) → Versicolor\n",
+ "\n",
+ "Test 3: [7.0, 3.2, 5.5, 2.0]\n",
+ "Prediksi: virginica\n",
+ "Alasan: Petal length = 5.5 cm (> 2.45) & petal width = 2.0 cm (> 1.75) → Virginica\n",
+ "\n",
+ "Test 4: [6.5, 3.0, 4.5, 1.75]\n",
+ "Prediksi: virginica\n",
+ "Alasan: Petal width = 1.75 cm (boundary case) → virginica\n",
+ "\n",
+ "Test 5: [8.0, 4.0, 6.0, 2.5]\n",
+ "Prediksi: virginica\n",
+ "Alasan: Semua fitur besar, terutama petal → virginica\n",
+ "\n",
+ "=== KESIMPULAN ===\n",
+ "1. SETOSA: Mudah diidentifikasi dengan petal length < 2.45 cm\n",
+ "2. VERSICOLOR: Petal length > 2.45 cm DAN petal width ≤ 1.75 cm\n",
+ "3. VIRGINICA: Petal length > 2.45 cm DAN petal width > 1.75 cm\n",
+ "4. Fitur PETAL lebih dominan daripada SEPAL dalam klasifikasi\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\nasyw\\OneDrive\\Desktop\\Infinite_Learning\\Tugas_27\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
+ " warnings.warn(\n",
+ "c:\\Users\\nasyw\\OneDrive\\Desktop\\Infinite_Learning\\Tugas_27\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
+ " warnings.warn(\n",
+ "c:\\Users\\nasyw\\OneDrive\\Desktop\\Infinite_Learning\\Tugas_27\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
+ " warnings.warn(\n",
+ "c:\\Users\\nasyw\\OneDrive\\Desktop\\Infinite_Learning\\Tugas_27\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
+ " warnings.warn(\n",
+ "c:\\Users\\nasyw\\OneDrive\\Desktop\\Infinite_Learning\\Tugas_27\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Percobaan prediksi untuk berbagai spesies Iris\n",
+ "print(\"=== PERCOBAAN PREDIKSI IRIS ===\\n\")\n",
+ "\n",
+ "# Test 1: Karakteristik Setosa (petal kecil)\n",
+ "test1 = [5.0, 3.5, 1.5, 0.2] # petal length kecil = setosa\n",
+ "pred1 = predict_iris(test1)\n",
+ "print(f\"Test 1: {test1}\")\n",
+ "print(f\"Prediksi: {pred1}\")\n",
+ "print(f\"Alasan: Petal length = 1.5 cm (< 2.45) → Setosa\\n\")\n",
+ "\n",
+ "# Test 2: Karakteristik Versicolor (petal sedang)\n",
+ "test2 = [6.0, 3.0, 4.0, 1.3] # petal length sedang, width kecil = versicolor \n",
+ "pred2 = predict_iris(test2)\n",
+ "print(f\"Test 2: {test2}\")\n",
+ "print(f\"Prediksi: {pred2}\")\n",
+ "print(f\"Alasan: Petal length = 4.0 cm (> 2.45) & petal width = 1.3 cm (≤ 1.75) → Versicolor\\n\")\n",
+ "\n",
+ "# Test 3: Karakteristik Virginica (petal besar)\n",
+ "test3 = [7.0, 3.2, 5.5, 2.0] # petal length besar, width besar = virginica\n",
+ "pred3 = predict_iris(test3)\n",
+ "print(f\"Test 3: {test3}\")\n",
+ "print(f\"Prediksi: {pred3}\")\n",
+ "print(f\"Alasan: Petal length = 5.5 cm (> 2.45) & petal width = 2.0 cm (> 1.75) → Virginica\\n\")\n",
+ "\n",
+ "# Test 4: Edge case - petal width di batas\n",
+ "test4 = [6.5, 3.0, 4.5, 1.75] # petal width tepat di threshold\n",
+ "pred4 = predict_iris(test4)\n",
+ "print(f\"Test 4: {test4}\")\n",
+ "print(f\"Prediksi: {pred4}\")\n",
+ "print(f\"Alasan: Petal width = 1.75 cm (boundary case) → {pred4}\\n\")\n",
+ "\n",
+ "# Test 5: Extreme case - semua fitur besar\n",
+ "test5 = [8.0, 4.0, 6.0, 2.5] # semua fitur besar = virginica\n",
+ "pred5 = predict_iris(test5)\n",
+ "print(f\"Test 5: {test5}\")\n",
+ "print(f\"Prediksi: {pred5}\")\n",
+ "print(f\"Alasan: Semua fitur besar, terutama petal → {pred5}\")\n",
+ "\n",
+ "print(\"\\n=== KESIMPULAN ===\")\n",
+ "print(\"1. SETOSA: Mudah diidentifikasi dengan petal length < 2.45 cm\")\n",
+ "print(\"2. VERSICOLOR: Petal length > 2.45 cm DAN petal width ≤ 1.75 cm\") \n",
+ "print(\"3. VIRGINICA: Petal length > 2.45 cm DAN petal width > 1.75 cm\")\n",
+ "print(\"4. Fitur PETAL lebih dominan daripada SEPAL dalam klasifikasi\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eCvHkmPXdwTS"
+ },
+ "source": [
+ "### TODO #5 :\n",
+ "\n",
+ "Isi insight, berikan beberapa kali percobaan untuk mengklasifikasikan beberapa spesies bunga Iris berbeda berdasarkan \"sepal length, sepal width, petal length, petal width\".\n",
+ "\n",
+ "**Insight:**\n",
+ "Berdasarkan 5 percobaan prediksi yang dilakukan:\n",
+ "\n",
+ "1. Setosa (petal length 1.5 cm) - Berhasil diprediksi\n",
+ "2. Versicolor (petal length 4.0, width 1.3 cm) - Berhasil diprediksi \n",
+ "3. Virginica (petal length 5.5, width 2.0 cm) - Berhasil diprediksi\n",
+ "4. Boundary case (petal width 1.75 cm) - Diprediksi sebagai Virginica\n",
+ "5. Extreme case (semua fitur besar) - Diprediksi sebagai Virginica\n",
+ "\n",
+ "**Pola klasifikasi:**\n",
+ "- Petal length < 2.45 cm → Setosa\n",
+ "- Petal length > 2.45 cm & petal width ≤ 1.75 cm → Versicolor\n",
+ "- Petal length > 2.45 cm & petal width > 1.75 cm → Virginica\n",
+ "\n",
+ "Model menunjukkan konsistensi tinggi dengan aturan Decision Tree."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 10. Save Model\n",
+ "\n",
+ "Simpan model yang sudah dilatih untuk digunakan di Flask API atau aplikasi lain."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "✅ Model berhasil disimpan sebagai: iris_decision_tree_model.pkl\n",
+ "��� Lokasi file: c:\\Users\\nasyw\\OneDrive\\Desktop\\Infinite_Learning\\Tugas_27\\iris_decision_tree_model.pkl\n",
+ "📊 Ukuran file: 3.22 KB\n",
+ "ℹ️ Dataset info disimpan sebagai: iris_dataset_info.pkl\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Import joblib untuk save model\n",
+ "import joblib\n",
+ "import os\n",
+ "\n",
+ "# Simpan model yang sudah dilatih\n",
+ "model_filename = 'iris_decision_tree_model.pkl'\n",
+ "joblib.dump(model, model_filename)\n",
+ "\n",
+ "print(f\"✅ Model berhasil disimpan sebagai: {model_filename}\")\n",
+ "print(f\"📁 Lokasi file: {os.path.abspath(model_filename)}\")\n",
+ "\n",
+ "# Verifikasi ukuran file\n",
+ "file_size = os.path.getsize(model_filename) / 1024 # dalam KB\n",
+ "print(f\"📊 Ukuran file: {file_size:.2f} KB\")\n",
+ "\n",
+ "# Opsional: Simpan juga dataset info untuk referensi\n",
+ "iris_info = {\n",
+ " 'feature_names': list(iris.feature_names), # Sudah list, jadi cukup convert ke list\n",
+ " 'target_names': list(iris.target_names), # Sudah list, jadi cukup convert ke list\n",
+ " 'n_samples': len(iris.data),\n",
+ " 'n_features': len(iris.feature_names)\n",
+ "}\n",
+ "\n",
+ "info_filename = 'iris_dataset_info.pkl'\n",
+ "joblib.dump(iris_info, info_filename)\n",
+ "print(f\"ℹ️ Dataset info disimpan sebagai: {info_filename}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "=== TEST LOAD MODEL ===\n",
+ "✅ Model berhasil dimuat kembali\n",
+ "🌲 Model type: DecisionTreeClassifier\n",
+ "📏 Tree depth: 6\n",
+ "🍃 Number of leaves: 10\n",
+ "\n",
+ "🧪 Test Prediksi:\n",
+ "Input: [5.1, 3.5, 1.4, 0.2]\n",
+ "Prediksi: setosa\n",
+ "Confidence: 100.00%\n",
+ "\n",
+ "✅ Model siap digunakan untuk Flask API!\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\nasyw\\OneDrive\\Desktop\\Infinite_Learning\\Tugas_27\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
+ " warnings.warn(\n",
+ "c:\\Users\\nasyw\\OneDrive\\Desktop\\Infinite_Learning\\Tugas_27\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Test load model untuk memastikan model tersimpan dengan benar\n",
+ "print(\"\\n=== TEST LOAD MODEL ===\")\n",
+ "\n",
+ "# Load model yang baru disimpan\n",
+ "loaded_model = joblib.load(model_filename)\n",
+ "loaded_info = joblib.load(info_filename)\n",
+ "\n",
+ "print(f\"✅ Model berhasil dimuat kembali\")\n",
+ "print(f\"🌲 Model type: {type(loaded_model).__name__}\")\n",
+ "print(f\"📏 Tree depth: {loaded_model.get_depth()}\")\n",
+ "print(f\"🍃 Number of leaves: {loaded_model.get_n_leaves()}\")\n",
+ "\n",
+ "# Test prediksi dengan model yang dimuat\n",
+ "test_sample = [5.1, 3.5, 1.4, 0.2] # Sample Setosa\n",
+ "prediction = loaded_model.predict([test_sample])[0]\n",
+ "prediction_proba = loaded_model.predict_proba([test_sample])[0]\n",
+ "\n",
+ "print(f\"\\n🧪 Test Prediksi:\")\n",
+ "print(f\"Input: {test_sample}\")\n",
+ "print(f\"Prediksi: {loaded_info['target_names'][prediction]}\")\n",
+ "print(f\"Confidence: {prediction_proba[prediction]:.2%}\")\n",
+ "\n",
+ "print(f\"\\n✅ Model siap digunakan untuk Flask API!\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": ".venv",
+ "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": "3.10.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}