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Select a model, adjust its hyperparameters via intuitive sliders and dropdowns, and instantly visualize performance metrics and training results. The app supports both **classification** and **regression** tasks.\n\n---\n\n## Features\n\n![Screencastfrom2024-02-1520-17-54-ezgif com-video-to-gif-converter](https://github.com/9Xxi8Q4f/Web_App-MACHINE-LEARNING-ALGS/assets/89272933/e5085dbb-bde8-4520-9166-ec63c246a9a2)\n\n![Screenshot from 2024-02-15 20-17-06](https://github.com/9Xxi8Q4f/Web_App-MACHINE-LEARNING-ALGS/assets/89272933/e8b1e7f3-2c6a-4cfb-b4c5-7766c7f006c5)\n\n- **Upload your own dataset** or select from built-in ready-to-use datasets\n- **Train multiple models** with a single click — no code required\n- **Tune hyperparameters** interactively via the sidebar UI\n- **Visualize results** — training curves, confusion matrices, correlation heatmaps, and more\n- Supports both **classification** and **regression** tasks\n- Fully runs in the browser via **Streamlit**\n\n---\n## Supported Models\n\n| Model | Task |\n|---|---|\n| Deep Neural Network | Classification / Regression |\n| Decision Tree | Classification / Regression |\n| Linear Regression | Regression |\n| Logistic Regression | Classification |\n\n---\n\n## How to Use\n\n1. **Select a task** — choose Classification or Regression from the sidebar\n2. **Load data** — upload a CSV file or pick a built-in dataset\n3. **Choose a model** — select from the available ML / DL algorithms\n4. **Tune hyperparameters** — adjust settings interactively in the sidebar\n5. **Train \u0026 evaluate** — hit Run and explore the results and visualizations\n\n---\n\n\u003e Note: The entire application currently lives in a single file. Modularization into separate components (data loading, model definitions, visualization) is planned for a future update.\n\n---\n\n## Deploying\n\nThe app is not currently deployed. To run it yourself:\n\n- **Locally** — follow the Getting Started steps above\n- **Streamlit Cloud** — fork the repo, go to [share.streamlit.io](https://share.streamlit.io), connect your GitHub, and deploy `streamlit_ML_app.py` with one click — it's free\n\n---\n\n**All code is in one file so i need to modulate code.**\n\n---\n## Topics\n\n`machine-learning` `deep-learning` `streamlit` `web-application`\n`classification` `regression` `decision-trees` `linear-regression`\n`logistic-regression` `deep-neural-networks` `heatmap` `interactive`\n\n---\n\n## License\n\nThis project is licensed under the MIT License. See [LICENSE](LICENSE) for details.\n\n---\n\n\u003cdiv align=\"center\"\u003e\n  \u003ci\u003eBursa Uludağ University · Computer Engineering Department\u003c/i\u003e\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmozturan%2Fmlanddl-withwebapp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmozturan%2Fmlanddl-withwebapp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmozturan%2Fmlanddl-withwebapp/lists"}