https://github.com/mozturan/mlanddl-withwebapp
Web app to perfom machine learning algorithms (both for regression and classification) on almost ready datasets.
https://github.com/mozturan/mlanddl-withwebapp
decision-trees deep-learning deep-neural-networks heatmap linear-regression logistic-regression machine-learning streamlit web-application
Last synced: 2 months ago
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Web app to perfom machine learning algorithms (both for regression and classification) on almost ready datasets.
- Host: GitHub
- URL: https://github.com/mozturan/mlanddl-withwebapp
- Owner: mozturan
- License: mit
- Created: 2022-04-16T22:25:33.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2026-03-25T16:52:54.000Z (3 months ago)
- Last Synced: 2026-03-26T18:29:40.519Z (3 months ago)
- Topics: decision-trees, deep-learning, deep-neural-networks, heatmap, linear-regression, logistic-regression, machine-learning, streamlit, web-application
- Language: Python
- Homepage:
- Size: 27.3 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# ML & DL Explorer — Interactive Machine Learning Web App
---
## Overview
**ML & DL Explorer** is an interactive Streamlit web application that lets you train, tune, and evaluate machine learning and deep learning models — directly in your browser, with no coding required.
Upload your own dataset or use one of the built-in examples. 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.
---
## Features
- **Upload your own dataset** or select from built-in ready-to-use datasets
- **Train multiple models** with a single click — no code required
- **Tune hyperparameters** interactively via the sidebar UI
- **Visualize results** — training curves, confusion matrices, correlation heatmaps, and more
- Supports both **classification** and **regression** tasks
- Fully runs in the browser via **Streamlit**
---
## Supported Models
| Model | Task |
|---|---|
| Deep Neural Network | Classification / Regression |
| Decision Tree | Classification / Regression |
| Linear Regression | Regression |
| Logistic Regression | Classification |
---
## How to Use
1. **Select a task** — choose Classification or Regression from the sidebar
2. **Load data** — upload a CSV file or pick a built-in dataset
3. **Choose a model** — select from the available ML / DL algorithms
4. **Tune hyperparameters** — adjust settings interactively in the sidebar
5. **Train & evaluate** — hit Run and explore the results and visualizations
---
> 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.
---
## Deploying
The app is not currently deployed. To run it yourself:
- **Locally** — follow the Getting Started steps above
- **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
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**All code is in one file so i need to modulate code.**
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## Topics
`machine-learning` `deep-learning` `streamlit` `web-application`
`classification` `regression` `decision-trees` `linear-regression`
`logistic-regression` `deep-neural-networks` `heatmap` `interactive`
---
## License
This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.
---
Bursa Uludağ University · Computer Engineering Department