Instructions to use Poonguzhali25/california-housing-regression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Poonguzhali25/california-housing-regression with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Poonguzhali25/california-housing-regression", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
California Housing Price Prediction
This model predicts California housing prices using a Random Forest Regressor.
Model Information
- Framework: scikit-learn
- Algorithm: RandomForestRegressor
- Dataset: California Housing Dataset
Evaluation Metrics
- MAE: 0.3658
- RMSE: 0.5434
- R2_Score: 0.7746
Deployment
This model was automatically trained, evaluated and deployed using a GitHub Actions CI/CD pipeline.
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