Network Slicing Recognition Project Overview

The Network Slicing Recognition project focuses on optimizing network slice allocation in 5G networks using advanced Automated Machine Learning (AutoML) and ensemble learning techniques. Leveraging a comprehensive dataset of network conditions and service needs, a predictive model was developed to accurately allocate network slices based on factors like bandwidth, latency, and data throughput. Utilizing AutoML for efficient model selection and tuning, and employing ensemble methods to enhance prediction accuracy, a robust and scalable solution was achieved. An application was built using Flask to provide users with a seamless interface for predicting network slice types. This initiative significantly improves network performance, resource efficiency, and Quality of Service (QoS), setting new standards for automated, data-driven network management in the evolving landscape of 5G telecommunications.