Predictive Analytics and Performance Optimization in Mobile Networks Using Machine Learning

Mobile and wireless networks are becoming increasingly complex and dynamic, with a growing number of connected devices and ever-increasing data demands. To address the challenges posed by these evolving networks, predictive analytics and performance optimization techniques have gained considerable attention. This paper explores the applications of machine learning in predictive analytics and performance optimization for mobile/wireless networks. The study uses Least Absolute Shrinkage and Selection Operator(LASSO) algorithms to extract meaningful insights from historical network data and predict future network behavior. These predictions enable proactive decision-making, resource allocation, and optimization strategies to enhance network performance and user experience. These models enable accurate predictions by analyzing historical data, allowing network operators to allocate resources efficiently and mitigate potential issues before they impact network performance. The paper also discusses the implications of adopting machine learning techniques in realistic network environments, addressing data privacy, security, and interpretability of models. Further research is required to address the challenges and exploit the full potential of machine learning in this context.