Đánh giá các yếu tố ảnh hưởng tới khả năng có việc làm của sinh viên đại học sau khi ra trường

Tóm tắt

In the context of the labor market is volatile, requires candidates to have the ability and skills to match the needs of the employer. However, most recent graduates have difficulty finding jobs that match their abilities. At the same time, assessing the ability of candidates and selecting the right candidates for the job is a huge challenge for the employers. In this paper, we introduce a new approach to early prediction the employable of students after graduation by using some machine learning methods: Stochastic Gradient Descent, Decision Tree, Support Vector Machine and Artificial Neural Network. Experimental results show that, ANN and RF methods bring the best performance to predict student employment status with accuracy up to 70%.