HR analytics: Employee attrition analysis using logistic regression

Irwan Setiawan · Suprihanto Suprihanto · A C Nugraha · Jonatan Hutahaean

54 citations0/0 files storedIOP Conference Series Materials Science and Engineering

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AI and HR Technologies Employee Performance and Management Customer churn and segmentation

Abstract

Abstract Employee attrition can become a serious issue because of the impacts on the organization’s competitive advantage. It can become costly for an organization. The cost of employee attrition would be the cost related to the human resources life cycle, lost knowledge, employee morale, and organizational culture. This study aimed to analyze employee attrition using logistic regression. The result obtained can be used by the management to understand what modifications they should perform to the workplace to get most of their workers to stay. The data for the study were around four thousand employees, covering 261 days (one year working days) during 2015 — the data period between January and December. We use R for data integration, exploratory data analysis, data preparation, logistic regression, model evaluation, and visualization. The study has five steps: (1) data collection and business understanding, (2) data pre-processing, (3) exploratory data analysis, (4) model selection and training, and (5) test and evaluation of the model. The result of the study found eleven variables as key driving factors for employee attrition. It also showed that the model has 75 percent accuracy with 73 percent sensitivity and 75 percent specificity.

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References

2016An evidence-based review of HR Analytics 2016Employees and sustainability: the role of incentives 2018People Management Skills, Employee Attrition, and Manager Rewards: An Empirical Analysis 2016Human Resource Predictive Analytics (HRPA) For HR Management In Organizations

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2024Enhancing the Prediction of Employee Turnover With Knowledge Graphs and Explainable AI 2025A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition 2024Adoption of HR analytics to enhance employee retention in the workplace: A review

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