Analysis of Student Performance to Predict Career Specialization using Random Forest Data Mining Technique

Career specialization has emerged as a critical factor for students to attain success in today's highly competitive job market. Therefore, analyzing students' academic performance data to predict their career specialization can be a valuable tool for educational institutions. This study uses Random Forest to forecast graduates' job specialties. The study employed 360 records of IT graduates and the related grades from their technical/major subjects. The conducted tracer research was used to determine the graduates' present occupations. The model made use of 16 attributes in total. An accuracy rate of 87.19% was obtained from the prediction, which is quite good. The collected data also revealed that while students who perform fairly well in most of the major topics may have another relevant vocation, those who achieve very well will likely obtain a job as a developer.