Employee Attrition and Resignation Prediction for Healthcares





1. Figure




2. Goal

How to better understand the demand in the Healthcare job market.

To implement new plans to reduce turnover rates.

3. Methodology & Summary

  • KNN, Naive Bayes, Decision Tree and Random Forest models were used.

  • Logistic Regression, KNN,and Decision Trees were found to be suboptimal.

  • Overtime: Working Overtime is the biggest factor that helps predicting Attrition

  • Age: Younger employees are more likely to quit their current job

  • Studying and understanding the difference of younger employees from other age group


4. Code

Please click HERE for report.


5. References

Kaggle dataset: https://www.kaggle.com/datasets/jpmiller/employee-attrition-for-healthcare?resource=download “2023 NSI National Health Care Retention & RN Staffing Report.” NSI_National_Health_Care_Retention_Report.,

https://www.nsinursingsolutions.com/Documents/Library/NSI_National_Health_Care_Retention_Report.pdf.

Lecture Slides: Prasad Kislaya, Naive Bayes, Performance Measures ,Tree-Based Models in R, ClassificationFirst Look, K Nearest Neighbor (Revised)