Predicting Employee Attrition in the Saudi Private Sector Using Machine Learning

Employee attrition represents a prominent issue facing organizations, as human capital represents one of the most valuable resources. Attrition refers to the voluntary or involuntary reduction in the number of employees, which can negatively affect profitability, reputation, and overall organizational performance. Therefore, a comprehensive understanding of this phenomenon, its causal factors, and the development of retention strategies is crucial for mitigating employee turnover. The purpose of this work is to predict employee attrition in the Saudi private sector and identify the key factors contributing to employee turnover using machine learning approaches. in addition, the research structurally evaluates the performance of multiple Machine Learning (ML) algorithms within the proposed framework to determine the most effective predictive model for employee attrition. This study utilized a training dataset obtained from an online survey targeting employees in the Saudi private sector in order to investigate employee attrition and identify its most prominent causes within this context. Thus, various Machine Learning (ML) algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Bagging ensemble, and Voting Classifier (VC) were evaluated. The results demonstrate that the Voting Classifier (VC) yielded the highest accuracy at 90%. Moreover, the analysis identified job opportunities and job titles as some of the most influential factors driving employee turnover.