UNCOVERING HIDDEN PURCHASE PATTERNS: AN EXPLORATORY ANALYSIS IN RETAIL TRANSACTION

Market Basket Analysis (MBA) is an efficient data mining method that helps detect the connections among items that are regularly bought together in retail organizations. This paper examines MBA in the context of retail transactions by applying the FP-Growth algorithm. In the research, we have utilized data from online public sources containing tens of thousands of transactions. EDA, analysis of item frequencies, transactions' size distribution, as well as association rules mining techniques were used. Metrics such as support, confidence, and lift were calculated to assess the strength of the connection between the items. The result is that there are strong associations between such critical products as cereals, milk, and fruits, thus providing cross-selling opportunities and promotional bundles.