
Over a two-month period, contributed to the DataBytes-Organisation/DiscountMate_new repository by developing three core features focused on data-driven retail solutions. Built and integrated a large synthetic IGA supermarket dataset to enable privacy-conscious analytics and machine learning experimentation. Delivered a cart optimization system that helps users adhere to budget constraints and a smart substitution feature that recommends alternatives based on text similarity and preferences. Enhanced data quality and testing reliability by implementing data cleaning and synthetic data generation tooling using Python, R, and Pandas. The work established robust data pipelines and scalable experimentation frameworks for retail analytics and feature validation.
May 2025 — DataBytes-Organisation/DiscountMate_new: Delivered customer-focused cart optimization and smart substitution features to improve checkout value and budget adherence, plus data cleaning and synthetic data tooling to strengthen data quality and testing. These efforts reduce friction at the point of purchase, support robust analytics, and lay groundwork for scalable experimentation.
May 2025 — DataBytes-Organisation/DiscountMate_new: Delivered customer-focused cart optimization and smart substitution features to improve checkout value and budget adherence, plus data cleaning and synthetic data tooling to strengthen data quality and testing. These efforts reduce friction at the point of purchase, support robust analytics, and lay groundwork for scalable experimentation.
Concise monthly summary for April 2025 focused on features delivered, major improvements, and business impact.
Concise monthly summary for April 2025 focused on features delivered, major improvements, and business impact.

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