An Extensive Review and Comparison of Different Machine Learning Algorithms for Customer Behaviour Pattern Analysis

Consumer shopping behavior is changing due to the explosive growth of e-commerce, which brings both benefits and challenges. E-commerce enterprises must comprehend the shopping behavior of their clients to enhance user experiences and increase consumer satisfaction. This emphasizes how crucial it is to research consumer behavior in the context of online shopping. Customer transactions on e-commerce platforms produce enormous data from clicking, buying histories, demographics, and beyond. Integrating the findings from investigations offers a comprehensive understanding of buyers. In this paper, we have thoroughly analyzed and used various machine learning algorithms (Apriori algorithm, Ensemble learning, KNN, Naive Bayes, NLP) to analyze customer behavior. We have also evaluated the performance of various algorithms in terms of accuracy and time taken to analyze the data of consumers' shopping behavior.