The rapid growth of e-commerce has generated a large amount of transaction data with a high level of complexity. This data holds significant potential for further processing to uncover consumer purchasing behavior patterns. This study aimed to identify and analyze consumer purchasing patterns on e-commerce platforms through data analytics using the Market Basket Analysis approach. The data used in this study is simulated data compiled based on consumer purchasing trends over the past five years, with a focus on the fashion and beauty product categories. The analysis process was carried out by calculating support, confidence, and lift values to determine the relationship between products in a transaction. The research findings indicate a fairly strong relationship between several products, which can be used as a basis for developing marketing strategies, such as implementing product bundling and developing recommendation systems. Thus, this study contributes to optimizing the use of transaction data as a basis for data-driven decision-making.
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