Performance Prediction System for University Course Selection

Abstract

Due to the limited availability of human academic advisors, and the high demand for academic advising by students, students’ needs are not satisfied. In a digital world, collecting data has become increasingly important. Algorithms can be used for analysing data and building predictive models. More and more industries are using recommender systems to improve their services and personalize recommendations to satisfy every customer’s need better. Compared to humans, algorithms can also consider implicit data, which refers to information that is not explicitly stated but can be deducted from available data.