An Intelligent System for Predicting a User Access to a Web Based E-Learning System Using Web Mining

Abstract

In this Internet era, with ever-increasing interactions among participants, the size of the data is increasing so rapidly such that the information available to us in the near future is going to be unpredictable. Modeling and visualizing such data are one of the challenging tasks in the data analytics field. Therefore, business intelligence is the way in which a company can use data to improve business and operational efficiency whereas data analytics involves improving ways of making intelligence out of that data before acting on it. Thus, the proposed work focuses on prevailing challenges in data analytics and its application on social media like Facebook, Twitter, blogs, e-commerce, e-service and so on. Among all of the possible interactions, e-commerce, e-education, and e-services have been identified as important domains for analytics techniques. So, it focuses on machine learning technique in improving practice and research in such e-X domains. Empirical analysis is done to show the performance of proposed system using real-time datasets.