Keywords:
employee
churn prediction
artificial intelligence (AI)
big data
versatile black widow optimized driven logistic regression (VBW-LR)

  • Abstract

    Employee turnover is a significant threat to organizations and incurs tremendous costs and operational delays. Accurate prediction of employee churn enables companies to initiate proactive employee retention; however, data skewness, interpretability, and bias hinder accurate prediction. This paper presents a novel artificial intelligence-based approach for employee churn prediction based on big data analysis with the integration of a Versatile Black Widow-driven Logistic Regression (VBW-LR) model for predictive enhancement.  A publicly available Kaggle dataset of employee demographics, performance, and churn history was used. Preprocessing of data involved missing value handling, feature scaling, and reduction of dimensionality using Principal Component Analysis (PCA) to enhance the efficiency of the model. The VBW optimization algorithm, which is motivated by natural selection, was used to optimize the parameters of logistic regression using fine-tuning with the aim of maximizing predictive power. VBW-LR was used with Python to compare it with the best performance measures that yielded a high accuracy rate (92.5%), recall rate (91.2%), precision, and specificity (98.97%). Comparative research validated its more effective prediction capacity in identifying vulnerable workers over ordinary logistic regression. Through the strength of AI power and large-scale data analytics, this approach presents data-backed advice for HR practitioners through the mechanism of early intervention to reduce the risk of attrition and support more effective workforce retention techniques. Outcomes suggest that AI-based practices are most effective in managing a workforce and present a viable, scalable framework to minimize turnover from employees. By using these predictive models, organizations are provided with the ability to make decisions that create long-term stability and operational effectiveness.

  • References

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How to cite

Garg, P., Verma, Y., Singh, I., Raghu, N., Samal, A., & Sharma, N. (2025). Developing an advanced model for employee churn prediction leveraging AI and big data analytics. Multidisciplinary Science Journal, 7, 2025ss0209. https://doi.org/10.31893/multiscience.2025ss0209
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