A Predictive Model to Forecast Employee Churn for HR Analytics

14 pages•Published: May 1, 2023

Abstract

The challenge of employee churn is a major issue for most businesses and organizations. Unexpected employee departures can tarnish service delivery, harm customer loyalty, degrade quality of services, drop productivity, and hurt goodwill. The ability to predict employee churn is crucial for retaining valuable employees. This study proposes a predictive model that uses machine learning to forecast employee churn. The predictive model uses feature selection through Pearson correlation methods, information gain, and recursive feature elimination, combined with strong classification methods such as random forest, logistic regression, decision trees, gradient boosting machines, and K- nearest neighbours. The IBM dataset was used for training and testing the proposed predictive model. The accuracy of the different algorithms improved after applying particular feature selection methods. The results yielded showed that the random forest technique outperformed other models in terms of accuracy in the prediction of employee churn.

Keyphrases: Employee churn, feature selection methods, Machine Learning Models, prediction

In: Hossana Twinomurinzi, Nkosikhona Msweli, Tendani Mawela and Surendra Thakur (editors). Proceedings of NEMISA Digital Skills Conference 2023: Scaling Data Skills For Multidisciplinary Impact, vol 5, pages 17--30

Links:	https://easychair.org/publications/paper/q5sh
	https://doi.org/10.29007/528p