Nenice Akelo enhanced outlier detection for the DiscountMate_new repository by implementing a 14-day persistence rule using Python and Pandas. This feature ensures that only outliers persisting for at least two weeks are excluded from trend analyses, improving the reliability of data used for discount and promotion decisions. Nenice updated output file naming conventions to reflect the new exclusion logic, which aids in traceability and auditability throughout the data pipeline. The integration was completed with minimal disruption to existing workflows, demonstrating a focused approach to data analysis and outlier detection while addressing business needs for sustained data quality and transparency.