머신러닝을 활용한 취업 예측 모델 설계: D대학교 졸업생을 중심으로

Abstract

Recently, youth unemployment, especially the unemployment problem of university graduates, has emerged as a social problem. Unemployment of university graduates is both a pan-national issue and a university-level issue, and each university is makingmany efforts to increase the employment rate of graduates. In this study, we present a model that predicts employment availabilityof D-university graduates by utilizing Machine Learning. The variables used were analyzed using up to 138 personal information,admission information, bachelor’s information, etc., but in order to reflect them in the future curriculum, only the data after admission works effectively, so by department / student. The proposal was limited to the recommended ability to improve the separateemployment rate. In other words, since admission grades are indicators that cannot be improved due to individual efforts afterenrollment, they were used to improve the degree of prediction of employment rate. In this research, we implemented a employment prediction model through analysis of the core ability of D-University, which reflects the university’s philosophy, goals, humresources awards, etc., and machined the impact of the introduction of a new core ability prediction model on actual employment. Use learning to evaluate. Carried out. It is significant to establish a basis for improving the employment rate by applying the resultsof future research to the establishment of curriculums by department and guidance for student careers.