---
license: apache-2.0
tags:
- sklearn
- regression
- housing-prices
- california
- random-forest
---

# California Housing Price Prediction Model

Modèle de prédiction des prix immobiliers en Californie utilisant Random Forest Regressor.

## Performance

- **Test RMSE**: 46,834
- **Test MAE**: 31,292
- **CV RMSE**: 49,101

## Utilisation

```python
from huggingface_hub import hf_hub_download
import joblib

# Télécharger le modèle
model_path = hf_hub_download(repo_id="BinkyTwin/CaliforniaPrice", filename="final_release.joblib")
model_data = joblib.load(model_path)
model = model_data["final_model"]

# Faire une prédiction
# Le modèle attend un DataFrame avec les colonnes suivantes:
# - longitude, latitude, housing_median_age, total_rooms, total_bedrooms
# - population, households, median_income, ocean_proximity
# - rooms_per_household, bedrooms_per_room, population_per_household
```

## Métadonnées

- **Scikit-learn version**: 1.8.0
- **Python version**: 3.13.9
- **Random state**: 42