<![CDATA[The phenomenon of employee attrition has become a serious challenge for organizations, as it directly affects productivity, recruitment costs, and long-term performance stability. Understanding the factors that lead to employee turnover can no longer rely solely on manual observation; therefore, data-driven approaches are required to identify hidden patterns within workforce data. This study aims to predict employee attrition using the Random Forest algorithm applied to the IBM HR Analytics Employee Attrition & Performance dataset, which consists of 1,470 records and 35 attributes. The research stages include data preprocessing, handling class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE), model training, and performance evaluation using accuracy, precision, recall, F1-score, ROC-AUC, and a confusion matrix. The results indicate that the baseline model without SMOTE exhibits low recall for the attrition class, whereas the application of SMOTE significantly improves model performance, particularly for the minority class, achieving a final accuracy of 83.96%. The most influential features identified are Stock Option Level, MonthlyIncome, and JobSatisfaction. These findings provide a comprehensive understanding of the factors influencing employee attrition and can serve as a foundation for organizations in designing more adaptive and data-driven employee retention strategies.]]>
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