From 90f2caeba7843fbef953f367229aa69de8a4091f Mon Sep 17 00:00:00 2001 From: 202310715112 PUTRA AL RIFKI <202310715112@mhs.ubharajaya.ac.id> Date: Tue, 2 Dec 2025 10:46:55 +0700 Subject: [PATCH] Delete File Tugas/PolynomialRegression.ipynb --- File Tugas/PolynomialRegression.ipynb | 165 -------------------------- 1 file changed, 165 deletions(-) delete mode 100644 File Tugas/PolynomialRegression.ipynb diff --git a/File Tugas/PolynomialRegression.ipynb b/File Tugas/PolynomialRegression.ipynb deleted file mode 100644 index f79bd9e..0000000 --- a/File Tugas/PolynomialRegression.ipynb +++ /dev/null @@ -1,165 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "02e1d686-6bb5-42ad-87a2-40036c54b9e0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sedang memproses data...\n", - "Sedang melatih model Polynomial Regression (Degree 2)...\n", - "\n", - "========================================\n", - "HASIL EVALUASI (POLYNOMIAL DEGREE 2)\n", - "========================================\n", - "1. Single Split Test:\n", - " - R2 Score (Akurasi) : -0.3654\n", - " - RMSE (Error Kuadrat): 2.8060\n", - " - MAE (Rata-rata Error): 1.6331 poin\n", - "\n", - "2. Cross Validation (5-Fold):\n", - " - Skor per tes : [-1.46479088e+04 7.39100795e-02 1.21529017e-01 1.10146144e-01\n", - " 5.71513075e-02]\n", - " - Rata-rata R2 : -2929.5092\n", - " - Kestabilan : +/- 5859.1998\n", - "\n", - "========================================\n", - "Contoh Prediksi: Rating Asli 7.0 | Prediksi Poly 5.28\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.linear_model import LinearRegression\n", - "from sklearn.preprocessing import PolynomialFeatures, LabelEncoder\n", - "from sklearn.pipeline import make_pipeline\n", - "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n", - "\n", - "# ==========================================\n", - "# 1. LOAD DATA & PREPROCESSING\n", - "# ==========================================\n", - "print(\"Sedang memproses data...\")\n", - "df = pd.read_csv('Latest 2025 movies Datasets.csv')\n", - "# take a look at the dataset\n", - "df.head()\n", - "\n", - "df = df.dropna(subset=['release_date', 'vote_average', 'popularity', 'vote_count'])\n", - "df['release_date'] = pd.to_datetime(df['release_date'], errors='coerce')\n", - "df = df.dropna(subset=['release_date'])\n", - "\n", - "df['release_year'] = df['release_date'].dt.year\n", - "df['release_month'] = df['release_date'].dt.month\n", - "\n", - "le = LabelEncoder()\n", - "df['original_language_encoded'] = le.fit_transform(df['original_language'])\n", - "\n", - "features = ['popularity', 'vote_count', 'release_year', 'release_month', 'original_language_encoded']\n", - "X = df[features]\n", - "y = df['vote_average']\n", - "\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 (POLYNOMIAL DEGREE 2)\n", - "# ==========================================\n", - "degree = 2\n", - "print(f\"Sedang melatih model Polynomial Regression (Degree {degree})...\")\n", - "# Pipeline: Buat fitur pangkat -> Lalu Regresi Linear\n", - "model = make_pipeline(PolynomialFeatures(degree), LinearRegression())\n", - "model.fit(X_train, y_train)\n", - "\n", - "# ==========================================\n", - "# 3. EVALUASI LENGKAP\n", - "# ==========================================\n", - "print(\"\\n\" + \"=\"*40)\n", - "print(f\"HASIL EVALUASI (POLYNOMIAL DEGREE {degree})\")\n", - "print(\"=\"*40)\n", - "\n", - "# Prediksi data test\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}\")\n", - "print(f\" - RMSE (Error Kuadrat): {rmse:.4f}\")\n", - "print(f\" - MAE (Rata-rata Error): {mae:.4f} poin\")\n", - "\n", - "# Cross Validation (5-Fold)\n", - "print(f\"\\n2. Cross Validation (5-Fold):\")\n", - "# Hati-hati: Polynomial CV bisa agak lambat dibanding Linear biasa\n", - "cv_scores = cross_val_score(model, X, y, cv=5, scoring='r2')\n", - "\n", - "print(f\" - Skor per tes : {cv_scores}\")\n", - "print(f\" - Rata-rata R2 : {cv_scores.mean():.4f}\")\n", - "print(f\" - Kestabilan : +/- {cv_scores.std():.4f}\")\n", - "\n", - "# Contoh Prediksi\n", - "print(\"\\n\" + \"=\"*40)\n", - "print(f\"Contoh Prediksi: Rating Asli {y_test.iloc[0]} | Prediksi Poly {y_pred[0]:.2f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8d83a184-b95f-4b73-a67d-1d523923ee1f", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'pd' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", - "\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", - "\u001b[31mNameError\u001b[39m: name 'pd' is not defined" - ] - } - ], - "source": [ - "df = pd.read_csv(\"Latest 2025 movies Datasets.csv\")\n", - "\n", - "# take a look at the dataset\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9c933e26-cbc2-47e4-9f25-efbb65ef1d92", - "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 -}