A Machine Learning-based Career Recommendation

Abstract

Career selection is perhaps the most significant decision any student makes during their academic journey. This research thus presents a machine learning-based career recommendation system that will offer each student a career suggestion based on their academic performance and extracurricular involvement, including whether they hold a parttime job. Evaluations were conducted on several supervised machine learning models for predicting best career paths, such as Random Forest, Support Vector machine (SVM), and KNearest Neighbor (KNN). Experiments revealed that Random Forest performed best and had an accuracy of 93%. The proposed system assists students in making informed career decisions based on data analysis.