<![CDATA[Employee attrition is a major human-resource challenge because unexpected employee departures can increase recruitment and training costs, disrupt operational continuity, and reduce organizational productivity. This study investigates a machine-learning-based predictive analytics approach for employee attrition prediction using Decision Tree and Random Forest with 10-fold cross-validation. The study uses the IBM HR Analytics Employee Attrition dataset, comprising 1,470 employee records and 35 attributes. Data preprocessing includes target-label assignment, missing-value handling, categorical-to-numerical transformation, and normalization. The models are implemented in RapidMiner and evaluated using accuracy, precision, recall, and confusion matrix analysis. The results show that Random Forest outperforms Decision Tree, achieving an accuracy of 85.44% compared with 83.06% for Decision Tree. Random Forest also obtains higher recall for the Attrition = Yes class, increasing from 8.44% with Decision Tree to 14.35%. Despite the improvement, both models exhibit limited minority-class detection, indicating that class imbalance remains a key challenge. Tableau-based exploratory visualization further indicates that factors including MonthlyIncome, OverTime, JobSatisfaction, and TotalWorkingYears are associated with employee attrition. The findings demonstrate that predictive analytics can support data-driven employee-retention decision-making, while future work should address class imbalance and investigate more advanced classification and explainability techniques. ]]>
