A Comparative Review and Analysis of Association Rule Mining On Market Basket Data through Python

Abstract

Market Basket Analysis (MBA) is a data mining technique that can be applied in a variety of industries, including education, computational biology, health, advertising, and more. According to this hypothesis, the purchaser tries to deduce the relationship between the other items from the one they have just bought. They strive to arrange the objects in this manner. Data collection for distributors is the major goal of Market Basket Analysis (MBA) in fields like field marketing and nuclear science. Recognize how a customer makes purchases, Marketing analysts are signs that a business is aware of consumer behaviour. Making the business more successful is our responsibility. A new product will be developed by traders, and purchasers may also have an original idea. Also, many things can be envisioned by buyers. The selection of high-support legislation is based on the likelihood that it will be applicable in a sizable portion of probable transactions. Analyses of consumer baskets are one confidence where they are filtered i.e. biased if they don't satisfy the value considered. To examine customer behaviour, it is important to find out better association rule mining algorithms. In this paper, multiple association rules mining algorithms such as Apriori, FP Growth, Transaction Mapping, ECLAT, and LP Growth Algorithm are to be applied and compare the results of accuracy, support, and confidence values and find the better algorithm.