Developed an end-to-end forecasting capability for the cdcepi/FluSight-forecast-hub repository, enabling outbreak predictions based on historical data. The work focused on refining forecasting logic to improve both accuracy and methodology, supporting data-driven public health planning. Leveraging Python for implementation, the developer applied data analysis and forecasting techniques to deliver a production-ready feature with clear, incremental commits that ensured traceability and version control discipline. No major bug fixes were reported during this period, with efforts concentrated on feature delivery and methodological enhancements. The contribution provided a foundation for more accurate outbreak prediction workflows within the public health domain.