A Machine Learning-based Career Recommendation - Nayak, Vaishnavi; Vora, Neha (Journal of Trends in Computer Science and Smart Technology, 2024)
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 part-time job. Evaluations were conducted on several supervised machine learning models for predicting best career paths, such as Random Forest, Support Vector machine (SVM), and K-Nearest 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.