Predicting the Employment Destinations of University Students Based on Machine Learning Algorithms

Abstract

The research on the classification prediction of students' employment destinations in higher education not only opens up new application areas for classification algorithms, but also represents a new attempt to introduce machine learning algorithms into the analysis of employment guidance and the development of teaching systems. The purpose of this paper is to study the employment destination prediction of college students based on machine learning algorithms. The factors influencing the employability of university students are analysed and the XGBoost model in decision trees is explored. A graduate employment prediction algorithm based on HMIGW feature selection and XGBoost algorithm is proposed to predict the employment situation of the class of 2022 and the type of employment, and the experimental results show that the algorithm is able to obtain relatively accurate conclusions on graduate employment prediction.