DESIGN CUSTOMER BEHAVIOUR PREDICTION IN E-COMMERCE USING APRIORI ALGORITHM.

ABSTRACT In the fast-moving world of e-commerce, figuring out what customers want is super important. It can really help boost sales, make users happier, and even shape how businesses strategize. So, this research dives into a pretty cool method for predicting what folks are likely to buy — and it uses the Apriori algorithm, which is quite a go-to in the field of association rule mining. Basically, by analyzing past transaction data, this system can identify common item groups and uncover rules that show how different products are related. These insights are gold because they help businesses anticipate customer preferences and open the door for personalized recommendations. Isn’t that neat? The model we’re talking about here really sets the stage for smarter marketing, sharper inventory management, and a better overall customer experience. And when you look at the advantageous outcomes on the several transaction datasets of the experiment, it is apparent that this algorithm is capable of identifying actionable patterns and they are incredibly useful when it comes to making strategic decisions in e-commerce. The research actually emphasizes the prospect of rule-based learning in retail con systems more smart. In addition, it suggests some promising directions in further research, such as real-time below analysis cumulative reinforcement linking above learning models machine learning.