Applying Association Rules Mining Approach in Skills Competition based on APRIORI Algorithm
Mary Ellaine R. Cervantes, Daniel D. Dasig, Roel C. Traballo, Mary Ann B. Taduyo, Rudolph Val F. Guarin, Eleonora E. Claricia, Mengvi P. Gatpandan, Denver Jhon R. Calantoc, Christine Ferrer, Lester G. Diampoc, Ana Belen Cuyugan, Fernando I. Talion · 2022
In aid of delivering quality Information Technology Education in the Philippines and to upscale the quality of ITE in the country, organizations, and other universities have participated in extra-curricular and academic activities. The study aimed at determining the associated relationships among six hundred competitors and mentors in a skills competition using the APRIORI Algorithm. The CRISP-DM Model and Python programming language were utilized in the study. Accordingly, there were thirteen (13) predicted rules that will allow competing institutions to carefully study before joining the IT Skills Olympics Competition, as such, the rules set can guide each competing school to design a program for their competing teams and select category of the competition to compete in the future. The researchers recommend further empirical analysis using the dataset by employing other machine learning techniques and algorithms.