Web Traffic Analysis Using Machine Learning

Abstract

Web Traffic Analysis enhance the digital marketing strategies and user experience by analyzing website traffic, user engagement metrics, and customer behavior patterns. Time series analysis and forecasting in time series data finds it significance in many applications such as business, stock market and exchange, weather, medical, electricity demand, web traffic and user behavior, cost and usage of products such as fuels, electricity etc. Time series data contains a high volume of numerical data and a time dimension which captures a lot of information including inter-pattern trends and correlations. The Pattern Discovery Method (PDM) in Machine Learning is used for performing accurate analysis of time series data and making important decision. The Pattern Discovery Method which includes the process of pattern deploying and pattern evolving to improve the effectiveness of using and updating discovered patterns for finding relevant and forecasting information.