Predicting Employee Attrition: A Machine Learning Framework for Knowledge-Based Workforce Retention Strategies

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Employee attrition significantly threatens organisational knowledge retention and competitive performance. This study proposes a machine learning framework for predicting employee attrition, grounded within knowledge management and decision support theory. The framework integrates intelligent data preprocessing, feature engineering, SMOTETomek class balancing, ensemble learning and stratified five-fold cross-validation to ensure rigorous, leakage-free performance estimation. All proposed models significantly outperformed the logistic regression baseline across all metrics (paired [Formula: see text]-test, [Formula: see text]). The Deep Learning model achieved the highest accuracy (0.898) and recall (0.548), while the Voting Ensemble achieved the highest AUC-ROC (0.882), representing an 84% improvement in recall over baseline. Feature importance analysis identified income, age, tenure and manager relationships as chief attrition predictors. The proposed framework equips HR practitioners with actionable decision support tools to proactively manage talent and preserve organisational knowledge.