Research on the precise pointing of college students’ career based on logistic regression classification algorithm

Abstract

This study analyzes the career development direction of college students through logistic regression and gray theory to provide more accurate career planning guidance for college students. Logistic regression classification algorithm and gray theory are used to study career influencing factors and predict future career development trends. The significant influence of academic performance, English proficiency, and civic and political cultivation on career direction is identified by analyzing the sample data of S college students. The model’s accuracy is significantly higher than random guessing, up to 79.8%. The results show that the combination of logistic regression and gray theory can effectively predict the career direction of college students, provide data-supported career guidance services for colleges and universities, and help students make more reasonable career planning.

Keywords

• logistic regression classification algorithm
• gray theory
• career development
• influence factor
• 97m80