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
File size: 639 Bytes
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license: mit
library_name: scikit-learn
tags:
- sklearn
- random-forest
- regression
- california-housing
- mlops
---
# California Housing Price Prediction
This model predicts the median house value in California districts using a Random Forest Regressor.
The model is trained and deployed automatically using a GitHub Actions CI/CD pipeline.
## Model Information
- **Algorithm:** Random Forest Regressor
- **Framework:** Scikit-Learn
- **Dataset:** California Housing
## Evaluation Metrics
- **R2**: 0.7748
- **RMSE**: 0.5433
- **MAE**: 0.3657
## Pipeline
Data Preparation → Training → Evaluation → Automatic Deployment |