Machine learning model to predict used car prices in Tunisia
# Used Car Price Prediction - Tunisia
Machine learning project to predict used car prices using XGBoost and Random Forest models.
## Dataset Features
- **Target**: Prix (Price in TND)
- **Numerical**: Car age, Mileage, Engine power, Engine displacement
- **Categorical**: Fuel type, Brand, Model, Transmission
## Models
- XGBoost Regressor (primary)
- Random Forest Regressor
## Performance Metrics
- R² Score
- Adjusted R²
- RMSE (Root Mean Squared Error)
- MAE (Mean Absolute Error)
## Setup
```bash
pip install pandas numpy scikit-learn xgboost hyperopt matplotlib seaborn
```
## Usage
```python
jupyter notebook cava_ML.ipynb
```
## Files
- `cava_EDA.ipynb` - Exploratory Data Analysis
- `cava_ML.ipynb` - Model training and evaluation
- `cava_model_data.csv` - Processed dataset