Assessing the Impact of Student Employability Using Decision Tree Classifier in Education 4.0

Abstract

After graduation, student employability is a crucial issue that has an impact on people's lives, society, and the economy as a whole. Employers are looking for applicants in the present employment market who not only have the required technical abilities but also have the flexibility to adapt to shifting work conditions. In this study, investigate how a decision tree classifier machine learning approach affects graduates' employability in education 4.0. For this, a dataset is used for employability that contains a variety of variables, like academic achievement, real-world experience, and soft skills. Using the decision tree classifier approach to assess the dataset, predictions are then made regarding the factors that would affect employment after graduation. This study finds that the decision tree classifier, when compared to other machine learning algorithms, is more accurate and better suited for use in improving student employability by identifying the key competencies and characteristics needed for various job roles and matching them with qualified candidates in education 4.0.