Welcome to the ML Hyperparameter Optimization App! This Streamlit web application allows you to experiment with various regression algorithms and hyperparameters to optimize your machine learning model's performance.
- Regression Algorithms: Choose between Linear Regression, Ridge Regression, and Random Forest Regression.
- Hyperparameter Tuning: Experiment with different hyperparameters such as regularization strength and number of estimators to optimize model performance.
- Data Visualization: Visualize the hyperparameter tuning process and model performance with interactive plots generated using Plotly.
- Upload Your Dataset: Upload your CSV dataset or use the provided example dataset.
- Select Model and Set Parameters: Choose a regression model and set hyperparameters.
- Explore Model Performance: Visualize the optimization process and view model performance metrics such as coefficient of determination (R^2) and error (mean squared error or mean absolute error).
To run the app locally, follow these steps:
- Clone the repository:
git clone <repository-url> && cd <repository-name> - Install the required dependencies:
pip install -r requirements.txt - Run the app:
streamlit run app.py
An example CSV dataset is provided for demonstration purposes. You can use this dataset to explore the app's features without uploading your own data.
- Python
- Streamlit: For building the interactive web application.
- Pandas: For data manipulation and preprocessing.
- NumPy: For numerical computations.
- Plotly: For creating interactive data visualizations.
- scikit-learn: For implementing regression algorithms and hyperparameter tuning.
Contributions are welcome! If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request.