# An Experimental Analysis of Work-Life Balance Among The Employees using Machine Learning Classifiers

**Authors:** Karampudi Radha, Mekala Rohith  
**arXiv:** [2105.07837](https://arxiv.org/abs/2105.07837)  
**Date:** 2021-04-28  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2105.07837v1/

## Key points

- AI and ML are important in improving the quality of our lives, cities, and nations
- ML is useful in predicting effective working schedules and patterns for better work-life balance
- Researchers analyzed data from 12,756 employees to identify factors affecting work-life balance (WLB)
- Certain factors play a major role in WLB
- ML algorithms were trained using Random Forest Classifier, SVM, and Naive Bayes on 80% of the data
- The best result was 71.5% accuracy in predicting WLB
- AI and ML can be used to improve employee well-being by predicting effective working schedules and patterns
- Employers can make informed decisions about how best to support their workers based on understanding the factors that contribute to WLB
- Happier employees who are more productive and engaged at work could be achieved through this approach

## Summary

The importance of Artificial Intelligence (AI) and Machine Learning (ML) in our daily lives cannot not be overstated. Researchers have found that these technologies can be used to improve the quality of our lives, as well as cities and nations alike. One area where ML is proving particularly useful is in predicting effective working schedules and patterns that increase worker efficiency, ultimately leading to a better work-life balance. To achieve this, researchers analyzed data from 12,756 employees to identify factors affecting work-life balance (WLB). They found that certain factors play a major role in WLB and trained ML algorithms using Random Forest Classifier, SVM, and Naive Bayes on 80% of the data. The algorithms were then tested for accuracy in predicting WLB, with the best result being 71.5%. This study provides valuable insights into how AI and ML can be used to improve employee well-being by predicting effective working schedules and patterns. By understanding the factors that contribute to WLB, employers can make informed decisions about how best to support their workers. Ultimately, this could lead to happier employees who are more productive and engaged at work.