Customer Behaviour Prediction for E-Commerce Sites Using Web Mining and Machine Learning

This research addresses the challenge of understanding and predicting user behavior on websites through the application of advanced web mining and machine learning techniques. In today's digital world, businesses rely on websites to connect with customers and drive sales. However, understanding how customers behave on websites is a major challenge due to the variety and complexity of user interactions. Traditional methods of tracking website performance often fall short in capturing the detailed patterns of user behaviour, leading to missed opportunities for improvement. This research focuses on reorganizing websites by analysing how users interact, behave, and what they prefer. By using tools like analytics platforms and machine learning, businesses can create websites that better meet their audience's needs. Using data mining algorithms and predictive modelling, this study analyzes large-scale web interaction datasets to identify patterns and trends that drive user engagement and conversions. Techniques include feature engineering for interaction metrics, clustering for behavioural segmentation, and supervised learning models for forecasting user actions. There are several important benefits, such as scalable approaches to combining predictive models and insights for effective website performance, enhancements in user attendance, and conversion rates. Experimentally it has been shown that Web usage mining data is more reliable in predicting the user behavior on e-commerce websites.