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Nowadays, academic institutions conduct studies to attain quality and excellence in student academic performance through Data Mining tools. This paper explores various classification techniques in predicting graduates' career specialization. The data sets used were obtained from Bulacan State University Sarmiento Campus' Information Technology graduates from 2013 to 2016. From these data, a model was created using Naïve Bayes, J48, Random Forest, and Support Vector Machine classificationdoi:10.30534/ijatcse/2020/5391.32020 fatcat:5sd4dfyqsbhwrbexxcmn2is4km