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초록
영어
This research aims to use machine learning technology in human resource management to predict employees’ work-life balance. The study utilized a dataset from IBM Watson Analytics in the IBM Community for the machine learning analysis. Multinomial dependent variables concerning workers’ work-life balance were examined, categorized into continuous and categorical types using the Generalized Linear Model. The complexity of assessing variable roles and their varied impact based on the type of model used was highlighted. The study’s outcomes are academically and practically relevant, showcasing how machine learning can offer further understanding of psychological variables like work-life balance through analyzing employee profiles.
목차
ABSTRACT
Ⅰ. Introduction
Ⅱ. Literature Review on Work-Life Balance
Ⅲ. Methodology
3.1. Dataset
3.2. Generalized Linear Model
3.3. Preprocessing and Data Mining Models
Ⅳ. Results
4.1. Linear Regression Model
4.2. Binomial Classification Model
Ⅴ. Conclusions
5.1. Discussion
5.2. Research Contributions and Practical Implications
5.3. Limitations and Future Research Directions
Ⅰ. Introduction
Ⅱ. Literature Review on Work-Life Balance
Ⅲ. Methodology
3.1. Dataset
3.2. Generalized Linear Model
3.3. Preprocessing and Data Mining Models
Ⅳ. Results
4.1. Linear Regression Model
4.2. Binomial Classification Model
Ⅴ. Conclusions
5.1. Discussion
5.2. Research Contributions and Practical Implications
5.3. Limitations and Future Research Directions
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참고문헌
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