Worked on enhancing the BaseClassifier API documentation for the aeon-toolkit/aeon repository, focusing on improving clarity and developer experience. The main contribution involved updating Python docstrings to include detailed Raises sections, explicitly documenting potential exceptions such as ValueError for shape and channel mismatches in the predict and predict_proba methods. This approach aimed to make error handling expectations transparent, reducing onboarding time and minimizing runtime surprises for users. Additionally, implemented automatic pre-commit fixes to maintain consistent code formatting and linting. The work demonstrated attention to both software development best practices and the importance of clear, maintainable documentation in Python projects.