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The Cosmetic Product Layout Recommendation System successfully applies the Apriori and FP-Growth algorithms to analyze 1,900 cosmetic sales transactions involving more than 27 products. By identifying products that are frequently purchased together, the system generates effective product placement recommendations that can improve store layouts and increase cross-selling opportunities.
The comparative analysis demonstrates that FP-Growth generally outperforms Apriori in terms of computational efficiency, particularly when processing larger transaction datasets, due to its use of the FP-Tree structure rather than repeated candidate generation. Overall, the system serves as a practical decision support tool for retailers, enabling data-driven merchandising strategies that enhance both operational efficiency and customer shopping experience.
Project Summary
The comparative analysis demonstrates that FP-Growth generally outperforms Apriori in terms of computational efficiency, particularly when processing larger transaction datasets, due to its use of the FP-Tree structure rather than repeated candidate generation. Overall, the system serves as a practical decision support tool for retailers, enabling data-driven merchandising strategies that enhance both operational efficiency and customer shopping experience.
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