https://github.com/iamratinder/regularization-streamlit
This Streamlit app allows you to explore and visualize how different regularization methods (Ridge, Lasso, ElasticNet) affect model coefficients and performance on various regression datasets.
https://github.com/iamratinder/regularization-streamlit
regularization streamlit visualization
Last synced: about 1 month ago
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This Streamlit app allows you to explore and visualize how different regularization methods (Ridge, Lasso, ElasticNet) affect model coefficients and performance on various regression datasets.
- Host: GitHub
- URL: https://github.com/iamratinder/regularization-streamlit
- Owner: iamratinder
- Created: 2025-06-18T17:27:26.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-18T18:28:24.000Z (about 1 year ago)
- Last Synced: 2025-07-06T20:09:12.506Z (12 months ago)
- Topics: regularization, streamlit, visualization
- Language: Python
- Homepage: https://iamratinder-regularization-streamlit-app-2ok87f.streamlit.app/
- Size: 3.91 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Coefficient Playground: Ridge, Lasso & ElasticNet
This Streamlit app allows you to explore and visualize how different regularization methods (Ridge, Lasso, ElasticNet) affect model coefficients and performance on various regression datasets.
## Features
- Choose between California Housing, Diabetes, or a synthetic regression dataset.
- Adjust regularization strength (`alpha`) and L1 ratio (`l1_ratio` for ElasticNet).
- Visualize and compare model coefficients.
- See model performance metrics (R² Score, Mean Squared Error).
- Visualize predictions for a selected feature.
## Demo
Try the deployed app here:
[Visualize Regularization](https://iamratinder-regularization-streamlit-app-2ok87f.streamlit.app/)
Or open:
`https://iamratinder-regularization-streamlit-app-2ok87f.streamlit.app/`
NOTE : Use in Light Mode for better UI
## Setup
1. **Clone the repository** (or download the code):
```
git clone
cd regularization_Streamlit
```
2. **Install dependencies** (preferably in a virtual environment):
```
pip install -r requirements.txt
```
## Usage
Run the Streamlit app:
```
streamlit run app.py
```
Then open the provided local URL in your browser.
## Requirements
- Python 3.7+
- See `requirements.txt` for Python package dependencies.
## License
MIT License.
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