Next POILP: Next Point of Interest Location Prediction Using Machine Learning

It is essential to keep an eye on students as soon as they arrive on the university campus, day or night. As a result, students will frequently use a variety of university facilities, such as the cafeteria and library. Recently, several researchers have worked on this topic using Machine Learning (ML) and Non-Machine Learning (Non-ML) methods. In this study, 16 students served as the subjects, while the historical data came from 1641 entries in the web database of the Private Indian University. Logistic regression (LR) is used to predict the students' subsequent POIL (Point of Interest Location). When using Folds 5 to evaluate the LR model on historical data, considering 75% training data and 25% testing data, 60% accuracy for the target feature was reached. In addition, contrast the Support Vector Machine (SVM) and Random Forest algorithms with the LR model.