Predicting Employment Status and Types of University Graduates in South Korea Using Machine-Learning Techniques

In today’s competitive job market, coupled with rapid technological developments and global events like the pandemic, securing employment has become increasingly difficult for new graduates. Therefore, this study aims to identify the key features that influence the employment status (employed or unemployed) and type (regular or nonregular) of university graduates in South Korea using several machine learning methods including logistic regression, decision tree, random forest, XGBoost, support vector machine, neural network, and Naïve Bayes. Among these methods, XGBoost and neural network demonstrated the highest performance. We applied the SHAP (SHapley Additive exPlanations), one of the XAI (eXplainable AI) techniques, to the XGBoost model. The results revealed that cumulative GPA is the most significant factor influencing labor market outcomes. Furthermore, geographical location, including the location of the university, high school, and current city of residence, as well as family background factors such as parents’ occupation and income at the time of university admission, and current assets also played crucial roles. Overall, these results demonstrate the significant impact of academic performance, geographical location, and family background in shaping graduates’ employment outcomes. These findings offer valuable insights for policymakers, educational institutions, students, and their families to develop strategies and policies for improving employment prospects.