원문정보
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
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
저자정보
참고문헌
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