https://github.com/soumya-kushwaha/ml-model-explorer
ML Model Master is a web application built using Streamlit that allows you to explore and compare different machine learning classifiers on various datasets.
https://github.com/soumya-kushwaha/ml-model-explorer
machine-learning streamlit webapp
Last synced: about 2 months ago
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ML Model Master is a web application built using Streamlit that allows you to explore and compare different machine learning classifiers on various datasets.
- Host: GitHub
- URL: https://github.com/soumya-kushwaha/ml-model-explorer
- Owner: Soumya-Kushwaha
- License: apache-2.0
- Created: 2024-04-26T18:36:42.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-23T14:48:04.000Z (about 1 year ago)
- Last Synced: 2025-07-21T20:59:59.671Z (12 months ago)
- Topics: machine-learning, streamlit, webapp
- Language: Python
- Homepage: https://ml-model-explorer.streamlit.app/
- Size: 42 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# ML Model Explorer
## Overview
ML Model Explorer is an interactive web application built with Streamlit that allows users to experiment with different machine learning classifiers and understand their performance characteristics. The app provides a user-friendly interface for exploring popular datasets, testing various classification algorithms, and visualizing their results through multiple performance metrics.
Try it out live at [ml-model-explorer.streamlit.app](https://ml-model-explorer.streamlit.app)
## Features
### Dataset Selection
- Choose from classic machine learning datasets:
- Iris Dataset
- Breast Cancer Dataset
- Wine Dataset
### Supported Classifiers
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Decision Trees
- Random Forest
- Gradient Boosting
- Naive Bayes
### Interactive Model Tuning
- Real-time hyperparameter adjustment via intuitive sliders
- Classifier-specific parameter controls:
- Logistic Regression: C parameter
- KNN: number of neighbors (K)
- SVM: C parameter
- Decision Tree: maximum depth
- Random Forest: number of estimators and maximum depth
- Gradient Boosting: number of estimators and maximum depth
### Performance Analytics
- Comprehensive model evaluation metrics:
- Accuracy Score
- Precision Score
- Recall Score
- F1 Score
- Visual performance analysis:
- Interactive Confusion Matrix
- Detailed Classification Report
- ROC Curve (for binary classification)
## Quick Start
### Using the Live App
Visit [ml-model-explorer.streamlit.app](https://ml-model-explorer.streamlit.app) to try the application instantly in your browser.
### Running Locally
1. Clone the repository:
```bash
git clone https://github.com/yourusername/ml-model-explorer.git
cd ml-model-explorer
```
2. Install required dependencies:
```bash
pip install -r requirements.txt
```
3. Launch the application:
```bash
streamlit run src/main.py
```
## Dependencies
- numpy
- pandas
- matplotlib
- seaborn
- scikit-learn
- streamlit
## Usage
1. Select a dataset from the sidebar dropdown menu
2. Choose a classifier type
3. Adjust the hyperparameters using the interactive sliders
4. Click the "Predict" button to see the results
5. Explore the various performance metrics and visualizations
## Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Acknowledgments
- Built with Streamlit
- Uses scikit-learn's built-in datasets and classifiers
- Visualization powered by matplotlib and seaborn