Earticle

초록

영어

This study examines career trajectories among women with career breaks, using data from the 2019 National Survey of Women on Career Breaks (n=1,138). The data underwent preprocessing, including outlier detection, feature scaling, and class imbalance correction with SMOTEENN. Three machine learning models were evaluated, with the Random Forest model achieving the best performance. Key predictors included flexible leave policies, social insurance, remote work options, and job security. The findings highlight the importance of supportive organizational policies in retaining female employees. Future research should explore longitudinal impacts and additional variables like organizational culture.

목차

Abstract
1. Introduction
2. Materials & Methodology
2.1. Data Source
2.2 Data Cleaning and Preprocessing
2.3 Outlier Detection and Removal
2.4 Feature Scaling and Transformation
2.5 Handling Class Imbalance
2.6 Feature Selection
2.7 Boxplots of numerical features against the target variable
3. Model Training & Analysis
3.1 Model Evaluation
3.2 Results
4. Discussion
References

키워드

저자

• 변해원 [ Haewon Byeon | Dept. of AI-Software, Inje University, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620