Analysis of User Behavior Patterns using Machine Learning Algorithms

A weblog is a dynamic record of transactions regularly updated by website visitors. It contains a variety of information, such as IP addresses, status codes, bytes sent, categories, and timestamps. The primary purpose of a weblog is to monitor user behavior and classify their interests based on different categories and qualities. This study aims to achieve two main objectives: first, to categorize successful responses, and second, to distinguish between normal and abnormal user behavior. The research process outlined in this paper involves several steps. It begins with data collection, where relevant information is gathered from the weblogs. The next step is data pre-processing, which involves cleaning and organizing the data to make it suitable for analysis. The researchers then employ clustering techniques to identify different patterns of user activity, enabling a quick assessment and prediction of user behavior. The main focus of the research is user prediction, achieved by analyzing user preferences extracted from the weblogs at various levels. To accomplish this, the researchers utilize various machine learning techniques. One of the implemented models is the Random Forest Classifier, which is utilized to predict user behavior based on specific input parameters.