Employee Turnover Prediction Using Machine Learning Techniques

Organisations worldwide face significant costs and operational disruptions when employees leave, driving interest in predictive models that anticipate turnover before it occurs. Machine learning approaches process historical human resources data—ranging from demographics and compensation to performance metrics and engagement surveys—to identify patterns associated with departure. Core steps include data pre-processing to address missing values and class imbalance, feature selection to highlight the most influential factors, model training with algorithms such as decision trees, logistic regression, gradient boosting or neural networks, and rigorous evaluation using metrics like recall, precision and F1-score. Advances in explainability techniques and causality analysis have furthered understanding of why certain features drive turnover, supporting targeted interventions. Recent work has also emphasised the shift from “big data” to “deep data,” prioritising data quality and mixed-method designs to capture both quantitative indicators and contextual insights. Key drivers of attrition commonly identified include compensation changes, workload and overtime, career progression opportunities, job satisfaction, length of service and personal or geographic factors. Successful deployment of these models enables human resources teams to implement early retention strategies, tailor development plans and improve workforce stability across diverse industries.

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Employee Turnover Prediction Using Machine Learning Techniques publication trend

The graph below shows the total number of articles in employee turnover prediction using machine learning techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Machine learning classification: The process of training algorithms to assign categorical outcomes—such as “stay” or “leave”—based on input features.

Feature selection: Techniques used to identify and retain the most informative variables, reducing dimensionality and improving model performance.

Class imbalance: A data characteristic in which one outcome category (e.g., “leavers”) is much rarer than the other, requiring special handling to avoid biased predictions.

Ensemble learning: An approach that combines multiple base models to produce a single improved predictive system, often yielding greater accuracy and robustness.

Deep learning: A subset of machine learning involving neural networks with multiple hidden layers, capable of capturing complex nonlinear relationships in large datasets.

References

1. Predicting and explaining employee turnover intention. International Journal of Data Science and Analytics (2022).
2. From Big Data to Deep Data to Support People Analytics for Employee Attrition Prediction. IEEE Access (2021).
3. A Review of Employee Turnover Influence Factor and Countermeasure. Journal of Human Resource and Sustainability Studies (2016).
4. Employee Attrition Prediction Using Deep Neural Networks. Computers (2021).
5. Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry. Applied Sciences (2022).