  The division of one physical 5G communications infrastructure into several
virtual network slices with distinct characteristics such as bandwidth,
latency, reliability, security, and service quality is known as 5G network
slicing. Each slice is a separate logical network that meets the requirements
of specific services or use cases, such as virtual reality, gaming, autonomous
vehicles, or industrial automation. The network slice can be adjusted
dynamically to meet the changing demands of the service, resulting in a more
cost-effective and efficient approach to delivering diverse services and
applications over a shared infrastructure. This paper assesses various machine
learning techniques, including the logistic regression model, linear
discriminant model, k-nearest neighbor's model, decision tree model, random
forest model, SVC BernoulliNB model, and GaussianNB model, to investigate the
accuracy and precision of each model on detecting network slices. The report
also gives an overview of 5G network slicing.
