Delete File Tugas/PolynomialRegression.ipynb
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"cells": [
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"cell_type": "code",
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"execution_count": 2,
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"id": "02e1d686-6bb5-42ad-87a2-40036c54b9e0",
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sedang memproses data...\n",
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"Sedang melatih model Polynomial Regression (Degree 2)...\n",
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"\n",
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"========================================\n",
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"HASIL EVALUASI (POLYNOMIAL DEGREE 2)\n",
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"========================================\n",
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"1. Single Split Test:\n",
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" - R2 Score (Akurasi) : -0.3654\n",
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" - RMSE (Error Kuadrat): 2.8060\n",
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" - MAE (Rata-rata Error): 1.6331 poin\n",
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"\n",
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"2. Cross Validation (5-Fold):\n",
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" - Skor per tes : [-1.46479088e+04 7.39100795e-02 1.21529017e-01 1.10146144e-01\n",
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" 5.71513075e-02]\n",
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" - Rata-rata R2 : -2929.5092\n",
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" - Kestabilan : +/- 5859.1998\n",
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"\n",
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"========================================\n",
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"Contoh Prediksi: Rating Asli 7.0 | Prediksi Poly 5.28\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split, cross_val_score\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from sklearn.preprocessing import PolynomialFeatures, LabelEncoder\n",
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"from sklearn.pipeline import make_pipeline\n",
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"from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
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"\n",
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"# ==========================================\n",
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"# 1. LOAD DATA & PREPROCESSING\n",
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"# ==========================================\n",
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"print(\"Sedang memproses data...\")\n",
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"df = pd.read_csv('Latest 2025 movies Datasets.csv')\n",
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"# take a look at the dataset\n",
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"df.head()\n",
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"\n",
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"df = df.dropna(subset=['release_date', 'vote_average', 'popularity', 'vote_count'])\n",
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"df['release_date'] = pd.to_datetime(df['release_date'], errors='coerce')\n",
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"df = df.dropna(subset=['release_date'])\n",
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"\n",
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"df['release_year'] = df['release_date'].dt.year\n",
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"df['release_month'] = df['release_date'].dt.month\n",
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"\n",
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"le = LabelEncoder()\n",
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"df['original_language_encoded'] = le.fit_transform(df['original_language'])\n",
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"\n",
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"features = ['popularity', 'vote_count', 'release_year', 'release_month', 'original_language_encoded']\n",
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"X = df[features]\n",
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"y = df['vote_average']\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
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"\n",
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"# ==========================================\n",
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"# 2. TRAINING MODEL (POLYNOMIAL DEGREE 2)\n",
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"# ==========================================\n",
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"degree = 2\n",
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"print(f\"Sedang melatih model Polynomial Regression (Degree {degree})...\")\n",
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"# Pipeline: Buat fitur pangkat -> Lalu Regresi Linear\n",
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"model = make_pipeline(PolynomialFeatures(degree), LinearRegression())\n",
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"model.fit(X_train, y_train)\n",
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"\n",
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"# ==========================================\n",
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"# 3. EVALUASI LENGKAP\n",
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"# ==========================================\n",
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"print(\"\\n\" + \"=\"*40)\n",
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"print(f\"HASIL EVALUASI (POLYNOMIAL DEGREE {degree})\")\n",
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"print(\"=\"*40)\n",
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"\n",
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"# Prediksi data test\n",
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"y_pred = model.predict(X_test)\n",
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"\n",
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"r2 = r2_score(y_test, y_pred)\n",
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"rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
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"mae = mean_absolute_error(y_test, y_pred)\n",
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"\n",
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"print(f\"1. Single Split Test:\")\n",
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"print(f\" - R2 Score (Akurasi) : {r2:.4f}\")\n",
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"print(f\" - RMSE (Error Kuadrat): {rmse:.4f}\")\n",
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"print(f\" - MAE (Rata-rata Error): {mae:.4f} poin\")\n",
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"\n",
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"# Cross Validation (5-Fold)\n",
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"print(f\"\\n2. Cross Validation (5-Fold):\")\n",
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"# Hati-hati: Polynomial CV bisa agak lambat dibanding Linear biasa\n",
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"cv_scores = cross_val_score(model, X, y, cv=5, scoring='r2')\n",
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"\n",
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"print(f\" - Skor per tes : {cv_scores}\")\n",
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"print(f\" - Rata-rata R2 : {cv_scores.mean():.4f}\")\n",
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"print(f\" - Kestabilan : +/- {cv_scores.std():.4f}\")\n",
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"\n",
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"# Contoh Prediksi\n",
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"print(\"\\n\" + \"=\"*40)\n",
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"print(f\"Contoh Prediksi: Rating Asli {y_test.iloc[0]} | Prediksi Poly {y_pred[0]:.2f}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "8d83a184-b95f-4b73-a67d-1d523923ee1f",
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'pd' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m df = \u001b[43mpd\u001b[49m.read_csv(\u001b[33m\"\u001b[39m\u001b[33mLatest 2025 movies Datasets.csv\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# take a look at the dataset\u001b[39;00m\n\u001b[32m 4\u001b[39m df.head()\n",
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"\u001b[31mNameError\u001b[39m: name 'pd' is not defined"
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]
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}
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],
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"source": [
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"df = pd.read_csv(\"Latest 2025 movies Datasets.csv\")\n",
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"\n",
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"# take a look at the dataset\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9c933e26-cbc2-47e4-9f25-efbb65ef1d92",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"pygments_lexer": "ipython3",
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"version": "3.12.0"
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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