{"id":26188334,"url":"https://github.com/soumya-kushwaha/ml-model-explorer","last_synced_at":"2026-05-10T16:38:25.618Z","repository":{"id":236542624,"uuid":"792473921","full_name":"Soumya-Kushwaha/ML-Model-Explorer","owner":"Soumya-Kushwaha","description":"ML Model Master is a web application built using Streamlit that allows you to explore and compare different machine learning classifiers on various datasets.","archived":false,"fork":false,"pushed_at":"2025-06-23T14:48:04.000Z","size":43,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-21T20:59:59.671Z","etag":null,"topics":["machine-learning","streamlit","webapp"],"latest_commit_sha":null,"homepage":"https://ml-model-explorer.streamlit.app/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Soumya-Kushwaha.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-04-26T18:36:42.000Z","updated_at":"2025-02-14T10:57:11.000Z","dependencies_parsed_at":"2024-04-27T17:43:13.976Z","dependency_job_id":null,"html_url":"https://github.com/Soumya-Kushwaha/ML-Model-Explorer","commit_stats":null,"previous_names":["soumya-kushwaha/ml-model-master"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/Soumya-Kushwaha/ML-Model-Explorer","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soumya-Kushwaha%2FML-Model-Explorer","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soumya-Kushwaha%2FML-Model-Explorer/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soumya-Kushwaha%2FML-Model-Explorer/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soumya-Kushwaha%2FML-Model-Explorer/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Soumya-Kushwaha","download_url":"https://codeload.github.com/Soumya-Kushwaha/ML-Model-Explorer/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soumya-Kushwaha%2FML-Model-Explorer/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32864082,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-10T13:40:02.631Z","status":"ssl_error","status_checked_at":"2026-05-10T13:40:02.145Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","streamlit","webapp"],"created_at":"2025-03-11T23:58:07.612Z","updated_at":"2026-05-10T16:38:25.598Z","avatar_url":"https://github.com/Soumya-Kushwaha.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ML Model Explorer\n\n## Overview\nML 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.\n\nTry it out live at [ml-model-explorer.streamlit.app](https://ml-model-explorer.streamlit.app)\n\n## Features\n\n### Dataset Selection\n- Choose from classic machine learning datasets:\n  - Iris Dataset\n  - Breast Cancer Dataset\n  - Wine Dataset\n\n### Supported Classifiers\n- Logistic Regression\n- K-Nearest Neighbors (KNN)\n- Support Vector Machines (SVM)\n- Decision Trees\n- Random Forest\n- Gradient Boosting\n- Naive Bayes\n\n### Interactive Model Tuning\n- Real-time hyperparameter adjustment via intuitive sliders\n- Classifier-specific parameter controls:\n  - Logistic Regression: C parameter\n  - KNN: number of neighbors (K)\n  - SVM: C parameter\n  - Decision Tree: maximum depth\n  - Random Forest: number of estimators and maximum depth\n  - Gradient Boosting: number of estimators and maximum depth\n\n### Performance Analytics\n- Comprehensive model evaluation metrics:\n  - Accuracy Score\n  - Precision Score\n  - Recall Score\n  - F1 Score\n- Visual performance analysis:\n  - Interactive Confusion Matrix\n  - Detailed Classification Report\n  - ROC Curve (for binary classification)\n\n## Quick Start\n\n### Using the Live App\nVisit [ml-model-explorer.streamlit.app](https://ml-model-explorer.streamlit.app) to try the application instantly in your browser.\n\n### Running Locally\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/yourusername/ml-model-explorer.git\ncd ml-model-explorer\n```\n\n2. Install required dependencies:\n```bash\npip install -r requirements.txt\n```\n\n3. Launch the application:\n```bash\nstreamlit run src/main.py\n```\n\n## Dependencies\n- numpy\n- pandas\n- matplotlib\n- seaborn\n- scikit-learn\n- streamlit\n\n## Usage\n\n1. Select a dataset from the sidebar dropdown menu\n2. Choose a classifier type\n3. Adjust the hyperparameters using the interactive sliders\n4. Click the \"Predict\" button to see the results\n5. Explore the various performance metrics and visualizations\n\n## Contributing\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## Acknowledgments\n\n- Built with Streamlit\n- Uses scikit-learn's built-in datasets and classifiers\n- Visualization powered by matplotlib and seaborn\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoumya-kushwaha%2Fml-model-explorer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsoumya-kushwaha%2Fml-model-explorer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoumya-kushwaha%2Fml-model-explorer/lists"}