Skip to content

Latest commit

 

History

History
59 lines (38 loc) · 2.4 KB

CONTRIBUTING.md

File metadata and controls

59 lines (38 loc) · 2.4 KB
Raw

Contributing to ML-For-Flow-Prediction-Analytics

Thank you for considering contributing to the ML-For-Flow-Prediction-Analytics project! Your contributions help improve the analysis and make it more effective and valuable for the domain.

Code Contributions

If you would like to contribute code to the project, please follow these steps:

  1. Fork the repository on GitHub.

  2. Clone your forked repository to your local machine:

    git clone https://github.com/PralinKhaira/ML-For-Flow-Prediction-Analytics.git

  3. Create a new branch for your feature or bug fix:

    git checkout -b my-feature

  4. Make the necessary code changes and additions.

  5. Commit your changes with a descriptive commit message:

    git commit -m "Add feature: my new feature"

  6. Push your changes to your forked repository:

    git push origin my-feature

  7. Open a pull request (PR) against the main repository's main branch. Ensure that your PR provides a clear description of the changes you made and any relevant information.

Once your PR is submitted, it will be reviewed and discussed. If any changes are requested, make the updates and push them to your branch. The PR will be updated automatically. Once your contribution is approved, it will be merged into the main repository.

Bug Reports and Feature Requests

If you encounter a bug or have a feature request, please help us by submitting an issue on the GitHub repository. When submitting an issue, please include as much relevant information as possible, including steps to reproduce the bug or a detailed explanation of the requested feature.

Code Style

When contributing code, please follow these code style guidelines:

  • Use consistent indentation (e.g., 4 spaces or tabs).
  • Follow the existing code formatting style as closely as possible.
  • Write clear and concise comments and documentation.
  • Use meaningful variable and function names.
  • Write tests for new functionality and ensure that existing tests pass.

Dataset and Environment

To reproduce the analysis, you will need the continuous_factory_process.csv dataset. Ensure that the dataset is located in the appropriate directory and can be read by the code.

License

By contributing to the Continuous Factory Process Analysis project, you agree that your contributions will be licensed under the MIT License.

Thank you for your contribution!