A Comprehensive Framework for Network Traffic Analysis and Prediction Using Synthetic Data, Machine Learning, and Interactive Visualization

This research introduces an approach that merges intelligence (AI) machine learning and interactive visualization to enhance network traffic analysis and forecasting. Initially the framework generates network structures that mimic diverse network scenarios and traffic trends. It then educates sophisticated machine learning algorithms to anticipate network behavior and performance based on these simulations. The setup features a user web interface that allows users to monitor real time network dynamics assess performance metrics and engage with the data through interactive tools to apply practical insights, from these models. With the integration of data, predictive analytics and dynamic visualization network administrators now possess a set of tools for, in depth performance monitoring and proactive network administration.