Abstract
Market Basket Analysis (MBA) is an analytical technique used to identify relationships between items in purchasing transactions. This notebook uses retail transaction datasets and the Apriori algorithm to discover hidden associations and patterns that retailers can leverage in optimizing marketing strategies, store layouts, and product recommendations. Through initial data processing, data exploration, and application of the Apriori algorithm, this analysis succeeded in identifying various significant associations between items that are frequently purchased together. The results provide valuable insights for retailers to develop targeted promotions and improve customer shopping experiences, while emphasizing the importance of selecting the right parameters to obtain accurate and relevant results.
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