Instructions to use ramkrish07/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use ramkrish07/model with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("ramkrish07/model", "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
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: mit
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library_name: scikit-learn
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tags:
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- sklearn
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- random-forest
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- regression
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- california-housing
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- mlops
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---
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# California Housing Price Prediction
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This model predicts the median house value in California districts using a Random Forest Regressor.
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The model is trained and deployed automatically using a GitHub Actions CI/CD pipeline.
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## Model Information
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- **Algorithm:** Random Forest Regressor
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- **Framework:** Scikit-Learn
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- **Dataset:** California Housing
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## Evaluation Metrics
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- **R2**: 0.8019
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- **RMSE**: 0.5095
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- **MAE**: 0.3322
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## Pipeline
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Data Preparation → Training → Evaluation → Automatic Deployment
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