Market Basket Analysis (MBA) is a fundamental data mining technique widely used in retail and e-commerce to discover relationships between products that are frequently purchased together. This knowledge enables retailers to optimize product placement, design effective promotional campaigns, and enhance customer experience through personalized recommendations. Association rules mining, particularly using the Apriori algorithm, provides a systematic approach to identify such patterns by analyzing transaction data. The rules generated take the form “if {item A} is purchased, then {item B} is also likely to be purchased,” quantified by metrics such as support, confidence, and lift. This study analyzes a dataset of supermarket transactions containing 22 common grocery items. The objective is to extract meaningful association rules that can provide actionable insights for retail decision-making.

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