🎛️ 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.)

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🚀 Run Any Model App

1. Clone the repository:

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! 💡