Developed data availability enhancements for the drshahizan/HPDP repository by introducing both raw and cleaned datasets to support analytics and machine learning workflows. Leveraging skills in data analysis, data cleaning, and Python, the work established a reproducible data access pattern within the project, enabling consistent datasets for experimentation and model training. This approach improved data accessibility and accelerated onboarding for data scientists by providing ready-to-use assets for analysis and benchmarking. The contribution laid the foundation for future machine learning pipelines, focusing on reproducibility and efficiency, and addressed the need for streamlined data-driven development within the repository’s collaborative environment.