A Machine Learning-Based Computation Model for Career Inclination: Addressing the Need for Aptitude-Based Career Guidance

Abstract

In today's dynamic job market, the need for effective and personalized career guidance has never been more crucial. Traditional methods of career counseling often rely on static approaches, failing to account for individual aptitude and personal inclinations. This research aims to develop a machine learning-based computation model that predicts career paths based on an individual's aptitude. By integrating data from aptitude tests, academic performance, and psychometric assessments, the proposed model offers a data-driven approach to career guidance. The study employs supervised learning techniques, particularly neural networks and decision trees, to  create a model that adapts to the diverse needs of students. The results demonstrate a significant improvement in the accuracy and personalization of career recommendations compared to traditional methods. The implications of this research extend to career counselors, educational institutions, and students, offering a more tailored approach to career planning.