Abstract
Employee turnover is a significant problem in many organizations, resulting in increased costs, decreased productivity, and decreased morale. The aim of this research is to predict employee turnover using data analysis algorithms and develop a predictive model that can be used to identify employees who are at risk of leaving the organization. This study preprocesses data on employee turnover rates, employee demographics, employee satisfaction, and other relevant factors, and uses data analysis machine learning algorithms such as Logistic regression, Decision tree, and XGBoost to analyze the data and develop a predictive model. The result of the study helps impact on organizational performance by identifying at-risk employees and potentially saving costs associated with recruiting and training new employees. This study outlines the methodology, data sources, expected outcomes, potential impact, and potential limitations and challenges associated with this study. Furthermore, this dissertation provides a framework for further research in this field.
Publication Date
4-3-2024
Document Type
Master's Project
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research
Advisor
Ayman Ibrahim
Recommended Citation
Alshamsi, Aisha, "USING DATA ANALYTICS TO PREDICT GOVERNMENT EMPLOYEE TURNOVER" (2024). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12332
Campus
RIT Dubai
