Enhancing Consumer Behavior Prediction through Machine Learning Algorithms: A Comparative Study

Abstract

This study examines the domain of AI (ML) calculations to reveal their reverberating effects on foreseeing purchaser conduct. Utilizing a far-reaching dataset from a dynamic retail web-based business stage, we thoroughly assessed the ability of powerful ML methods, including strategic relapse, choice trees, irregular woodlands, support vector machines, and brain organizations. Our unflinching point is to uncover the quintessential ML approach that offers the greatest amount of exactness in anticipating client ways of behaving, consequently enabling associations with significant experiences to enhance client commitment and sustain direction. Encouraged by past exploration, enlightening the surprising adequacy of ML in areas traversing broadcast communications, web-based business, banking, and retail, where it has exposed client turnover, thwarted false exercises, improved stock control, and revealed shopper inclinations. Outfitted with this important information, organizations can open the way to illuminated decisions, lift consumer loyalty, and flood ahead in a persistent quest for the upper hand.