Development of the Machine Learning-based Employment Prediction Model for Internship Applicants

인턴십 지원자를 위한 기계학습기반 취업예측 모델 개발

  • Kim, Hyun Soo (Department of Industrial and Management Engineering, Myongji University) ;
  • Kim, Sunho (Department of Industrial and Management Engineering, Myongji University) ;
  • Kim, Do Hyun (Department of Industrial and Management Engineering, Myongji University)
  • 김현수 (명지대학교 산업경영공학과) ;
  • 김선호 (명지대학교 산업경영공학과) ;
  • 김도현 (명지대학교 산업경영공학과)
  • Received : 2022.06.16
  • Accepted : 2022.06.23
  • Published : 2022.06.30

Abstract

The employment prediction model proposed in this paper uses 16 independent variables, including self-introductions of M University students who applied for IPP and work-study internship, and 3 dependent variable data such as large companies, mid-sized companies, and unemployment. The employment prediction model for large companies was developed using Random Forest and Word2Vec with the result of F1_Weighted 82.4%. The employment prediction model for medium-sized companies and above was developed using Logistic Regression and Word2Vec with the result of F1_Weighted 73.24%. These two models can be actively used in predicting employment in large and medium-sized companies for M University students in the future.

Keywords

Acknowledgement

본 논문이 나오기까지 많은 도움을 준 최기정, 권정을, 박희준학생에게 깊은 고마움을 전합니다.

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