https://github.com/hetbhalani/hypertuna
Hyperparameter-Tuning for machine learning models using streamlit
https://github.com/hetbhalani/hypertuna
Last synced: 11 months ago
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Hyperparameter-Tuning for machine learning models using streamlit
- Host: GitHub
- URL: https://github.com/hetbhalani/hypertuna
- Owner: hetbhalani
- Created: 2025-07-29T16:12:28.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-08-05T03:52:35.000Z (11 months ago)
- Last Synced: 2025-08-05T05:23:23.359Z (11 months ago)
- Language: Jupyter Notebook
- Homepage: https://hyper-tuna.streamlit.app/
- Size: 427 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🎛️ ML Hyperparameter Tuning Playground
This project is a collection of **interactive Streamlit app** that let you **tune hyperparameters** of various classical machine learning models in real-time.
Each app focuses on:
- 🎯 Tuning model hyperparameters
- 🧪 Evaluating model performance
- 📈 Visualizing results (PCA, accuracy, confusion matrix, etc.)
---
## 🚀 Run Any Model App
### 1. Clone the repository:
```bash
git clone https://github.com/hetbhalani/HyperTuna.git
```
### 2. Install dependencies:
```
pip install -r requirements.txt
```
### 3. Run the App:
```
streamlit run app.py
```
## 📷 Screenshorts
## 🧠 Included Models
| Filename | Model | Type | Visuals / Outputs |
|--------------------|------------------------|----------------|---------------------------------|
| `knn.py` | K-Nearest Neighbors | Classification | Accuracy, Confusion Matrix, PCA |
| `decision_tree.py` | Decision Tree | Classification | Accuracy, Tree Depth, Heatmap |
| `random_forest.py` | Random Forest | Classification | Accuracy, Feature Importance |
| `xgboost.py` | XGBoost | Classification | Accuracy, Feature Importance |
| `svm.py` | Support Vector Machine | Classification | Accuracy, Heatmap |
| `k_means.py` | KMeans Clustering | Clustering | PCA Plot, Cluster Accuracy |
| `dbscan.py` | DBSCAN Clustering | Clustering | PCA Visualization |
## ✨ Features
🔧 Interactive Hyperparameter Tuning via sliders and dropdowns
📊 Live metrics: Accuracy, R² Score, Confusion Matrix, Cluster Performance
📉 Visualizations: PCA, Feature Importance, Heatmaps
🎓 Educational: Learn how tuning affects model performance
## 👨💻 Author
Built by Het Bhalani — feel free to connect or contribute!
inspired by - CampusX
## 🤝 Contribute
Feel free to **fork** this repository, **improve** the code, and make a **Pull Request** — your contributions are highly appreciated! 🚀
### 🔧 Here are some functionalities you can add:
- Add more ML models
- User can add csv file and based in that user can tune selected model
- Implement cross-validation for better evaluation
- Add export functionality for trained models (e.g., using `joblib`)
- Improve visualizations with more interactive plots (e.g., Plotly)
Let’s make this project better together! 💡