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regression datasets.\n\n## Features\n\n- Choose between California Housing, Diabetes, or a synthetic regression dataset.\n- Adjust regularization strength (`alpha`) and L1 ratio (`l1_ratio` for ElasticNet).\n- Visualize and compare model coefficients.\n- See model performance metrics (R² Score, Mean Squared Error).\n- Visualize predictions for a selected feature.\n\n## Demo\n\nTry the deployed app here:\n\n[Visualize Regularization](https://iamratinder-regularization-streamlit-app-2ok87f.streamlit.app/)\n\nOr open:  \n`https://iamratinder-regularization-streamlit-app-2ok87f.streamlit.app/`\n\nNOTE : Use in Light Mode for better UI\n\n## Setup\n\n1. **Clone the repository** (or download the code):\n\n   ```\n   git clone \u003crepo-url\u003e\n   cd regularization_Streamlit\n   ```\n\n2. **Install dependencies** (preferably in a virtual environment):\n\n   ```\n   pip install -r requirements.txt\n   ```\n\n## Usage\n\nRun the Streamlit app:\n\n```\nstreamlit run app.py\n```\n\nThen open the provided local URL in your browser.\n\n## Requirements\n\n- Python 3.7+\n- See `requirements.txt` for Python package dependencies.\n\n## License\n\nMIT License.\n\n---\n\nMade with ❤️ by OpenLearn.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiamratinder%2Fregularization-streamlit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fiamratinder%2Fregularization-streamlit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiamratinder%2Fregularization-streamlit/lists"}