Worked on the d2cml-ai/Data-Science-Python repository to deliver a feature focused on Pokemon analytics dataset preparation and notebook state synchronization. Leveraging Python, JSON, and Jupyter Notebooks, the work involved updating execution counts in two notebook cells to ensure alignment with the evolving dataset, thereby supporting reproducible data science workflows. Integrated a comprehensive Pikachu JSON payload to facilitate enhanced data analysis and visualization within the notebook environment. This approach improved onboarding and experimentation speed for Pokemon analytics tasks, while maintaining stability in the data workflows. No major bugs were addressed during this period, reflecting a focus on feature development and workflow reliability.