CLASSIFYING ONLINE USERS USING MACHINE LEARNING AND INFORMATION-SEEKING PATTERNS

We must categorize internet users based on their information-searching habits if we hope to improve digital experiences and gain more insight into people's online behavior. This study explores how machine learning techniques might be used to categorize people based on their search keywords, online content interactions, and web surfing patterns. Using techniques including decision trees, support vector machines, and neural networks, the study aims to differentiate between different user classifications, such as infrequent visitors, goal-driven clientele, and meticulous researchers. As stressed in the study, feature selection, data preparation, and model testing must all be carefully considered in order to produce trustworthy results. The results support the use of targeted ads, personalized content sharing, and enhanced strategies for promoting user interaction with digital platforms.