Market-Based Analysis and Recommendation System

In this project, we conducted a comprehensive exploratory and market basket analysis using point-of-sale data from a retail environment. The analysis began with identifying trends in product performance, customer purchase frequency, and overall sales behavior. We also applied association rule mining (Apriori algorithm) to uncover frequent itemsets and strong product associations. These patterns revealed valuable insights into customer buying habits, such as high-frequency product pairings and potential cross-sell opportunities. Based on the findings, we made data-driven recommendations including bundling frequently co-purchased products, removing consistently underperforming items, and reorganizing shelf space to optimize visibility and accessibility. Summarized the results in a presentation tailored for stakeholders, focusing on clear visuals, simplified metrics (support, confidence, lift), and actionable strategies to improve revenue and customer retention.