Career Recommendation Based on Feature Selection for Undergraduate Students Using Machine Learning Techniques

Undergraduate students worldwide face difficulties choosing the career paths that should stay with them for at least several years. It is widespread for graduates to work in jobs or join a career path they are not interested in. Also, sometimes these jobs do not suit the skills and preferences of undergraduates. On the other hand, some jobs require certain criteria and various skills that may not be available to some undergraduates. Although an undergraduate can study a major that he/she is interested in, this does not guarantee that he/she will be successful in his/her future career path. Undergraduates in various majors need advice on career paths that suit their skills and interests. When a graduate feels dissatisfied with his/her job, this dissatisfaction can impact his/her productivity and performance in his/her assigned tasks and job responsibilities. Moreover, the overall performance of the organization where these workers work can be negatively affected by having less talented and less motivated workers. As a result, in this paper, a recommendation system is designed and proposed to guide undergraduates in choosing the optimal career path. Various machine-learning techniques were used in the recommendation system. The proposed system was applied to two datasets related to Information Technology jobs; “Dataset A” consisted of 20,000 records and “Dataset B” consisted of 500 records. Feature selection techniques were applied on “Dataset A” to determine the most important features that enhance the accuracy of the proposed recommendation system. It has been shown that the random forests technique performed the best among the other machine learning techniques.