인턴십 지원자를 위한 기계학습기반 취업예측 모델 개발

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

  • 김현수 (명지대학교 산업경영공학과) ;
  • 김선호 (명지대학교 산업경영공학과) ;
  • 김도현 (명지대학교 산업경영공학과)
  • 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)
  • 투고 : 2022.06.16
  • 심사 : 2022.06.23
  • 발행 : 2022.06.30

초록

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.

키워드

과제정보

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

참고문헌

  1. Hyunsoo, K., Sunho, K., Sangjin, H., Minseok, C., and Youngsoo, S., "A Study on the Improvement of Employment Competency through Corporate Field Experience", J. of The Korean Society of Semiconductor & Display Technology, pp. 78, 2019.
  2. Seonyoung, E., "Derivation of a 4-year college student employment prediction model based on university employment support: Focusing on the case of H University", Hanyang Graduate School, pp. 1-78, 2017.
  3. Pilseon, C., and Insik, M., "Employment Prediction Model for College Graduates Using Machine Learning Technique", Study on Vocational Competency Development, Vol. 21(1), pp. 31-54, 2018.
  4. Donghoon, L., and Tae-hyung, K., "A Study on the Prediction Model for Job Seekers for College Graduation Using Machine Learning Technique", Korea Information System Research, Vol. 29, pp. 287-306, 2020.
  5. Breiman, L., "Random Forests, Prediction Games and Algorithms", pp. 1-33, 1999.
  6. Kichang, L., "Korean Embedding," Acorn Publishing Co., Ltd., 121p, 2020.
  7. Minji, B., and Namgyu, K., "Meaning-based search for similar overseas patents through Word2Vec learning", The Journal of the Korean IT Service Society, Vol. 17, pp. 129-142, 2018.
  8. Dowoo, K., and Myunghwan, K., "Classification of Korean Newspaper Articles Based on Convolutional Neutral Network Using Doc2Vec and Word2Vec", Vol. 44, pp. 742-747, 2017.
  9. Delvin, J., Chang, M, W., Lee, K., and Toutanpva, K., "BERT: Pre-training of deep bidirectional transformers for language understanding," arXiv preprint arXiv:1810. 04805, 2018.
  10. Gigon, N., Ji-ho Y., and Sikyun, L., "The Effect of University Activities on Labor Market Performance", Economic Development Study, Vol. 16, pp. 143-172, 2010.
  11. Wonseok, L., and Hyunhee, K., "Interpretable convolutional neural network model for yield prediction in semiconductor fabrication", Journal of the Korean Society for Data Information and Information Science, Vol. 31, pp. 691 - 720, 2020. https://doi.org/10.7465/jkdi.2020.31.5.691