MelkiMeriem/CarPrediction

A clean, production-ready version of the Car Price Prediction application. This project predicts vehicle prices for the Tunisian market using a trained Extra Trees machine learning model. # Car Price Prediction - Clean Project A clean, production-ready version of the Car Price Prediction application. This project predicts vehicle prices for the Tunisian market using a trained Extra Trees machine learning model. ## Quick Start ### Prerequisites - Python 3.11+ - Node.js 16+ ### Installation 1. **Install Python dependencies:** ```bash pip install -r requirements.txt ``` 2. **Install frontend dependencies:** ```bash cd frontend npm install ``` ### Running the Application **Terminal 1 - Start Backend:** ```bash python app.py ``` Backend runs on localhost **Terminal 2 - Start Frontend:** ```bash cd frontend npm run dev ``` Frontend runs on localhost ## Project Structure ``` Car-Prediction-Project-Clean/ ├── app.py # Flask API server ├── predictor.py # ML prediction module ├── requirements.txt # Python dependencies ├── models/ │ ├── extra_trees_tuned.pkl # Trained model │ └── encoders.pkl # Preprocessing encoders └── frontend/ ├── src/ │ ├── App.jsx │ ├── main.jsx │ ├── styles.css │ └── components/ │ ├── PredictionForm.jsx │ └── Results.jsx ├── index.html ├── package.json └── vite.config.js ``` ## API Endpoints - `GET /health` - Health check - `GET /api/brands` - Get supported brands - `POST /api/predict` - Predict price for a single vehicle - `POST /api/predict_batch` - Batch prediction ## Usage Example ```python from predictor import CarPricePredictor predictor = CarPricePredictor() result = predictor.predict( marque='BMW', modele='Série 3', annee=2021, kilometrage=45000, energie='Diesel', boite_vitesses='Automatique', puissance_fiscale=9 ) print(f"Estimated price: {result['prix_predit']:,.0f} DT") ``` ## License Educational and research purposes. # CarPrediction