Comparison of Machine Learning algorithms for venue presence with inclusion of neighbours

User presence determination for being inside a venue, such that the user is provided with possible value-added services, is of high significance. It will get more prominent as we move to 5G and 6G networks’ rollout as we’ll get further means to have better aids. In this paper, machine learning (ML) algorithms computation results are obtained and analysed. Such algorithms would be candidate to be deployed for finding the confidence in decision making for a user’s location with respect to a venue. Number of UEs (User Equipment) are simultaneously placed inside and outside a venue and kept over a longer duration. Data such as received reference signal received power for serving cells and neighbour candidate cells etc. data is collected by each UE. The different available neighbours’ level in each data set is analysed. k-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) algorithms are used to find the accuracy based on neighbours’ depth among the available info. Very convincing results are observed over different level of neighbours being included in each Machine Learning (ML) algorithms.