Career Prediction Application using Machine Learning for Skill-Oriented Domains

Over the years, there has been a gradual increase in the global youth population, which has resulted in a greater labor force. However, there has been a growing concern about employing graduates in colleges and universities because of their inadequate basic skills required to fulfill positions that come with responsibilities. As a way to assist college students, especially during their capstone years, in making decisions regarding their career paths, this study developed an AI Career Prediction App called Mentor Map. This program implements Decision Tree and Support Vector Machine (SVM) strategies to skillfully assess one’s abilities, education, and other pertinent variables that aid in the creation of a predictive model. After gathering data through the appropriate means, educated hypotheses are made with regard to the most suitable career alternatives with respect to the users’ competencies, interests, and aspirations. This research captures the processes of conceptualizing the model, its methods of training and evaluation, as well as the results obtained from the reliability and accuracy verification tests of the application as a career prediction system. Further, analyses for this study focus on changing users’ decision-and planning-making behavior at the interface of postsecondary education and working life.