From 3faa263ba5959b48d5284a1fdfd6ff6095ceacc0 Mon Sep 17 00:00:00 2001 From: 202310715112 PUTRA AL RIFKI <202310715112@mhs.ubharajaya.ac.id> Date: Tue, 2 Dec 2025 10:46:37 +0700 Subject: [PATCH] Delete File Tugas/DecisionTree.ipynb --- File Tugas/DecisionTree.ipynb | 173 ---------------------------------- 1 file changed, 173 deletions(-) delete mode 100644 File Tugas/DecisionTree.ipynb diff --git a/File Tugas/DecisionTree.ipynb b/File Tugas/DecisionTree.ipynb deleted file mode 100644 index 0599da5..0000000 --- a/File Tugas/DecisionTree.ipynb +++ /dev/null @@ -1,173 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "e29b569c-b6a4-4eff-898d-ba939193228d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sedang memproses data...\n", - "Sedang melatih model Decision Tree...\n", - "\n", - "========================================\n", - "HASIL EVALUASI (DECISION TREE)\n", - "========================================\n", - "1. Single Split Test:\n", - " - R2 Score (Akurasi) : 0.8059 (80.59%)\n", - " - RMSE (Error Kuadrat): 1.0580\n", - " - MAE (Rata-rata Error): 0.7046 poin\n", - "\n", - "2. Cross Validation (5-Fold):\n", - " - Skor per fold : [0.5092657 0.74560943 0.78916584 0.80808243 0.81677625]\n", - " - Rata-rata R2 : 0.7338\n", - " - Kestabilan : +/- 0.1149\n", - "\n", - "========================================\n", - "Contoh Prediksi:\n", - " - Rating Asli : 7.0\n", - " - Prediksi Model : 6.19\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.model_selection import train_test_split, cross_val_score\n", - "from sklearn.tree import DecisionTreeRegressor\n", - "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n", - "from sklearn.preprocessing import LabelEncoder\n", - "\n", - "# ==========================================\n", - "# 1. LOAD DATA & PREPROCESSING\n", - "# ==========================================\n", - "print(\"Sedang memproses data...\")\n", - "\n", - "df = pd.read_csv('Latest 2025 movies Datasets.csv')\n", - "\n", - "# Membersihkan data: pastikan kolom penting tidak kosong\n", - "required_cols = ['release_date', 'vote_average', 'popularity', 'vote_count', 'original_language']\n", - "df = df.dropna(subset=required_cols)\n", - "\n", - "# Konversi release_date ke datetime\n", - "df['release_date'] = pd.to_datetime(df['release_date'], errors='coerce')\n", - "df = df.dropna(subset=['release_date']) # hapus yang gagal konversi\n", - "\n", - "# Feature Engineering\n", - "df['release_year'] = df['release_date'].dt.year\n", - "df['release_month'] = df['release_date'].dt.month\n", - "\n", - "# Encoding original_language\n", - "le = LabelEncoder()\n", - "df['original_language_encoded'] = le.fit_transform(df['original_language'])\n", - "\n", - "# Menentukan Fitur & Target\n", - "features = ['popularity', 'vote_count', 'release_year', 'release_month', 'original_language_encoded']\n", - "X = df[features]\n", - "y = df['vote_average']\n", - "\n", - "# Split Data (80% train, 20% test)\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "\n", - "# ==========================================\n", - "# 2. TRAINING MODEL (DECISION TREE)\n", - "# ==========================================\n", - "print(\"Sedang melatih model Decision Tree...\")\n", - "\n", - "model = DecisionTreeRegressor(max_depth=5, random_state=42)\n", - "model.fit(X_train, y_train)\n", - "\n", - "# ==========================================\n", - "# 3. EVALUASI LENGKAP & CROSS VALIDATION\n", - "# ==========================================\n", - "print(\"\\n\" + \"=\"*40)\n", - "print(\"HASIL EVALUASI (DECISION TREE)\")\n", - "print(\"=\"*40)\n", - "\n", - "# A. Evaluasi Single Split (Test Set)\n", - "y_pred = model.predict(X_test)\n", - "\n", - "r2 = r2_score(y_test, y_pred)\n", - "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n", - "mae = mean_absolute_error(y_test, y_pred)\n", - "\n", - "print(f\"1. Single Split Test:\")\n", - "print(f\" - R2 Score (Akurasi) : {r2:.4f} ({r2*100:.2f}%)\")\n", - "print(f\" - RMSE (Error Kuadrat): {rmse:.4f}\")\n", - "print(f\" - MAE (Rata-rata Error): {mae:.4f} poin\")\n", - "\n", - "# B. Cross Validation (5-Fold)\n", - "print(f\"\\n2. Cross Validation (5-Fold):\")\n", - "cv_scores = cross_val_score(model, X, y, cv=5, scoring='r2')\n", - "print(f\" - Skor per fold : {cv_scores}\")\n", - "print(f\" - Rata-rata R2 : {cv_scores.mean():.4f}\")\n", - "print(f\" - Kestabilan : +/- {cv_scores.std():.4f}\")\n", - "\n", - "# C. Contoh Prediksi\n", - "print(\"\\n\" + \"=\"*40)\n", - "sample_index = y_test.index[0] # pastikan index asli\n", - "print(f\"Contoh Prediksi:\")\n", - "print(f\" - Rating Asli : {y_test.loc[sample_index]}\")\n", - "print(f\" - Prediksi Model : {y_pred[0]:.2f}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e15a4120-6a82-4d24-a90b-a0b6df3e59db", - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d8d987a-7a3a-4601-a22c-7ed3b486d288", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ce678cc-f5cb-461f-aaad-b9a25ce0ec40", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c7ad7ba-191e-472a-97a9-3870b5ee7f93", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}