A Comparison the Effectiveness of Data Mining Techniques in Predicting Career Advice to Promote Education for People with Disabilities (Thailand)

The purpose of this research was to study and compare the effectiveness of data mining techniques used to predict career advice given to promote education to people with disabilities in Thailand. The study used data about job positions and the anonymized individual competencies of 907 disabled people who are currently studying in the Thai education system. The data consisted of 28 features of individual competencies, including proficiencies, occupations, special abilities, gender, and work positions, including major field and position recruited for. The study showed that: (i) random forest, decision tree, and bagging techniques were the most appropriate data mining techniques; and (ii) the three techniques showed precision levels of respectively 86.42%, 81.29%, and 81.07%, and so random forest methods were the most accurate.