Market-Basket Optimisation using Sales Pattern of Supermarket

Finding association among numerous items that are related together can be challenging. At times such association may vary from individuals. Large supermarkets are often faced with such a puzzle which if well addressed can boost or adversely affect the business space, time and profit. In this study, the use of a priority algorithm has been applied using the market pattern of items that are sold as related item in big supermarket like Shoprite etc to optimize item arrangement. We implemented the approach of the algorithm in Python language with hypothetical sales pattern of items from such supermarket, we obtained support, confidence, and lift as criteria from the sales pattern that gave the association rules from the customers.
The results of the sales pattern can be applied to rearrange items in the big supermarket for improved packaging, faster sales and resource utilization of such market.

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.