Elective Recommendation System

Choosing elective courses is an important part of academic planning for pre-final year students. The wrong electives can hurt students’ academic and professional journeys. For example, misaligned electives can lead to a lack of essential skill development, making graduates ill-equipped for their desired careers. Outdated or irrelevant electives can also result in a dearth of up-to-date knowledge, hindering one’s competitiveness in the job market. A paper presents a system that can help students make better choices about their elective courses. The system uses historical data from previous years to recommend elective courses to students based on their academic records and personal preferences. An accuracy of 92 has been achieved when implemented using a deep learning model (neural network) as it was able to capture the non-linear correlations in the data by the multilayered neural network, which means that it is very likely to recommend courses that are a good fit for students. It has the potential to help students make better choices about their electives, which can lead to a more rewarding educational experience and the development of skills that are relevant to their desired careers.