ML Playground is an interactive Streamlit web application that allows you to explore, train, and evaluate machine learning models without writing extensive code. Whether you're a beginner, student, or experienced data scientist, this tool provides an easy and interactive way to experiment with different models and datasets.
✅ Supports Classification & Regression: Train models on various datasets.
✅ Built-in Datasets & Custom Uploads: Work with Iris, Titanic, Wine, Breast Cancer, Boston Housing, Diabetes, California Housing, and more!
✅ Multiple ML Models: Train and compare algorithms from scikit-learn effortlessly.
✅ Interactive Data Visualization: Understand data with insightful visualizations.
✅ User-Friendly UI: No coding required – just select, train, and analyze!
🔹 Dataset Selection: Choose built-in datasets or upload your own CSV.
🔹 Problem Type Switching: Seamlessly switch between classification and regression.
🔹 Model Training: Select and train multiple models with a few clicks.
🔹 Performance Metrics: Evaluate models with accuracy, RMSE, R² scores, and more.
🔹 Model Comparison: Visualize model performance for better decision-making.
🔹 Prediction Interface: Make predictions with trained models interactively.
🔹 Custom Settings: Adjust test set split, random state, and feature scaling.
1️⃣ Select Dataset & Problem Type (Classification or Regression)
2️⃣ Choose ML Models from scikit-learn
3️⃣ Train & Evaluate Models using performance metrics
4️⃣ Compare Results & Make Predictions
Ensure you have the following installed:
- Python 3.9+
- pip (Python package manager)
- Git (for cloning the repository)
$ git clone https://github.com/mohd-faizy/Machine-Learning-Playground.git
$ cd Machine-Learning-Playground$ pip install -r requirements.txt$ streamlit run ml_playground.pyThe application will launch in your browser at http://localhost:8501/.
ML-Playground/
├── .vscode/ # VSCode settings (optional)
├── assets/ # Images, icons, or other static assets
├── saved_models/ # Folder for storing trained models
├── venv/ # Python virtual environment (optional)
├── ml_playground.py # Main Streamlit application file
├── requirements.txt # Python dependencies
└── README.md # Project documentation
-
Classification Datasets:
- Iris
- Titanic
- Wine
- Breast Cancer
- Digits
- Custom CSV Upload
-
Regression Datasets:
- Boston Housing
- Diabetes
- California Housing
- Custom CSV Upload
- Logistic Regression
- Decision Tree
- Random Forest
- k-Nearest Neighbors
- Support Vector Machine
- Gradient Boosting
- Neural Network (MLP)
- 1️⃣ Select Dataset & Problem Type → Choose from built-in datasets or upload a CSV.
- 2️⃣ Configure Settings → Adjust test size, random state, and scaling options.
- 3️⃣ Train Multiple Models → Select models and start training.
- 4️⃣ Analyze Performance → Get detailed metrics, charts, and comparisons.
- 5️⃣ Make Predictions → Use trained models to make real-time predictions.
| Technology | Purpose |
|---|---|
| Streamlit | Interactive web UI |
| scikit-learn | Machine learning models |
| Pandas | Data manipulation |
| NumPy | Numerical computations |
| Matplotlib | Data visualization |
| Seaborn | Statistical plotting |
| Joblib | Model persistence |
| Plotly | Interactive plots |
Want to improve this project? Follow these steps:
- Fork the repository
- Create a new branch (
git checkout -b feature-name) - Make changes and commit (
git commit -m "Added feature XYZ") - Push to your fork (
git push origin feature-name) - Create a Pull Request 🚀
This project is licensed under the MIT License. See the LICENSE file for details.
If you like this project, consider giving it a ⭐ on GitHub!
➤ If you have questions or feedback, feel free to reach out!!!
