Machine Learning Based Product Comparison for E-Commerce Websites

Online shopping through e-commerce has gained widespread popularity among consumers, revolutionizing the operations of businesses in global market. This paper examines benefits e-commerce, such as its convenience and ease comparing prices products, well difficulty customers may encounter when selecting optimal product. To overcome this difficulty, suggests a real-time online consumer behavior prediction system that anticipates visitor's purchasing intent using session visitor data assesses effectiveness Continuous Learning with Naive Bayes strategy. The article also focuses on developing recommendation strikes balance between increasing precision safeguarding users' privacy, utilizing Prize dataset to assess system's accuracy. Additionally, delves into domain opinion mining, outlining objectives responsibilities, anticipating sentiment, summarizing aspect-based predicting helpfulness feedback reviews.