Study and Prediction Analysis of the Employee Turnover using Machine Learning Approaches

Chakraborty, Raj; Mridha, Krishna; Shaw, Rabindra Nath; Ghosh, Ankush

doi:10.1109/gucon50781.2021.9573759

Cited by 33 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In further studies, Shikha N. Khera and Divya (2019) [13] and R. Chakraborty & K. Mridha [14] emphasized the predictive power of SVM and Random Forest in the Indian IT industry and HR analytics, respectively.…”

Section: Atef Et Al (2022)mentioning

confidence: 99%

Comparative Study of Employee Turnover Analysis Methods

Singh,

Jain,

Ranjan

2024

Int. J. Res. Publ. Rev.

View full text Add to dashboard Cite

In the rapidly evolving landscape of human resource management, understanding and mitigating employee turnover has emerged as a paramount challenge. This research delves into a comparative analysis of various machine learning algorithms applied to the same dataset over several research papers, with the objective of identifying the most effective method for predicting employee turnover. Through a meticulous evaluation based on accuracy, F1 score, and ROC value, this study aspires to identify the best methods for predicting employee turnover.This study highlights the Artificial Neural Network (ANN) as the top algorithm for predicting employee turnover due to its outstanding performance across multiple metrics, while XGBoost and LGBM Regression are also identified to be highly effective, providing a good mix of efficiency, interpretability, and reliability.• Performance Metrics: The evaluation of algorithms is based on accuracy, F1 score, and ROC AUC score. While these metrics provide a robust framework for comparison, they might not capture all aspects of model performance relevant to turnover prediction, such as calibration and the cost-sensitive nature of false positives versus false negatives in real-world HR decision-making.

show abstract

Section: Atef Et Al (2022)mentioning

confidence: 99%

Comparative Study of Employee Turnover Analysis Methods

Singh,

Jain,

Ranjan

2024

Int. J. Res. Publ. Rev.

View full text Add to dashboard Cite

show abstract

“…Research on employee turnover is an important and extensive organizational issue Vasantham and Aithal (2022); Sai and Pinapati (2023), as the shortage of skilled labour incurs significant costs for organizations (Matemani, 2019). High employee turnover rates can result in numerous adverse consequences for organizations, with one direct implication being the incurrence of both direct and indirect costs associated with HRM Practices (Hassanpour et al, 2022;Chakraborty et al, 2021). Shaikh et al (2020) also emphasize the significance of low turnover rates and the potential outcomes of employee turnover for organizations.…”

Section: Previous Study On Ti As a Dependent Variablementioning

confidence: 99%

The Impact of Human Resource Management Practices on Turnover Intention: A Literature Review

Wenyan,

Arshad,

Chenshu

2024

IJAREMS

View full text Add to dashboard Cite

The investigation into the correlation between human resource management practices (HRM Practices) and turnover intention (TI) holds paramount significance in organizational contexts. By unraveling this relationship, valuable insights emerge, guiding the formulation of strategies aimed at curtailing turnover rates and fortifying organizational stability. Through an exhaustive analysis encompassing diverse studies, this comprehensive review intricately examines the nexus between HRM Practices and TI. It consolidates findings, underscoring the pivotal role of effective HRMP in mitigating turnover rates, amplifying employee satisfaction, and nurturing sustained organizational development. Despite its illuminating contributions, the review conscientiously acknowledges inherent limitations, advocating for further scrutiny across varying industries and cultural landscapes. As an indispensable resource, it equips scholars, managers, and decision-makers with theoretical depth and practical acumen, empowering them to confront turnover challenges adeptly and propel the advancement of HRM strategies with precision and efficacy.

show abstract

“…Machine learning algorithms can identify employees who are likely to leave an organization by analyzing data on factors such as job satisfaction, career progression, and engagement. This provides human resource professionals with the opportunity to intervene and reduce employee turnover [5]. This can be accomplished through the use of targeted interventions such as providing employees with opportunities for training and development or addressing particular concerns that workers might have.…”

Section: Introductionmentioning

confidence: 99%

Potential of Artificial Intelligence in Boosting Employee Retention in the Human Resource Industry

Paigude

Pangarkar

Hundekari

et al. 2023

IJRITCC

View full text Add to dashboard Cite

Artificial intelligence (AI) has the potential to transform the human resource (HR) industry by automating routine tasks, improving decision-making, and enhancing employee engagement and retention. In this paper, we explore the use of machine learning and deep learning techniques to boost employee retention in the HR industry. We review the current state of the art in AI for HR, including the use of predictive analytics, natural language processing, and chatbots for talent management and employee development. We also discuss the challenges and ethical considerations of using AI in HR, including issues of bias and the need for transparent and explainable algorithms. Finally, we present case studies of successful AI-powered HR initiatives that have demonstrated improvements in employee retention and engagement. Our findings suggest that AI has the potential to significantly enhance employee retention in the HR industry, but its implementation requires careful planning and consideration of potential risks and ethical issues.

show abstract

Study and Prediction Analysis of the Employee Turnover using Machine Learning Approaches

Cited by 33 publications

References 8 publications

Comparative Study of Employee Turnover Analysis Methods

Comparative Study of Employee Turnover Analysis Methods

The Impact of Human Resource Management Practices on Turnover Intention: A Literature Review

Potential of Artificial Intelligence in Boosting Employee Retention in the Human Resource Industry

Contact Info

Product

Resources

About