| 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 | |