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Graduate Employment Quality and College Training Evaluation Based on Machine Learning

Author(s)

Yuling Liu, Can Hou, Shaoyong Hong

Affiliation(s)

School of Artificial Intelligence, Guangzhou Huashang College, Guangzhou, Guangdong, China

Abstract

This study aimed to analyze the influencing factors of talent cultivation quality on the employability of college students. With the increasing demand for applied talents, the education model of colleges and universities is facing challenges. This article collected employment information from 2628 graduates through a questionnaire survey, focusing on the impact of course design, practical training, and teaching on the quality of students' employment. The Lasso model and decision tree analysis method are used to mine and process data. Decision tree analysis helps identify the main factors that affect employability and has strong interpretability. The results indicate that the combination of practical courses and professional knowledge improves students' employability, and soft skills (communication skills) also play a key role in the employment process. According to the results of this study, suggestions for improving the talent-training mode in universities are proposed. To provide empirical support for significantly improving the employment competitiveness of graduates.

Keywords

Employability; Talent Training in Colleges; Lasso Model; Decision Tree; Factor Analysis

References

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