Machine Learning Approach for Employee Attrition Analysis

Kamath, R. S.; Jamsandekar, Shruti S.; Naik, Pavankumar

doi:10.31142/ijtsrd23065

Cited by 12 publications

(3 citation statements)

References 3 publications

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“…The process of identifying the existing talent in an organization is among the top talent management challenges. Kamath, Jamsandekar, and Naik (2019) posit that talent management involves a lot of managerial decisions to allocate the right people with the right skills. Higher Education institutions need to remain competitive, thus they strive to retain talented individuals.…”

Section: Employee Attrition and Talent Managementmentioning

confidence: 99%

Gender Differences in Employee Attrition at the Durban University of Technology

2023

ISEA

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Employee attrition is a critical issue facing many organisations including higher education institutions. With the outbreak of the Coronavirus disease, employee attrition rate has accelerated across universities in South Africa and Durban University of Technology is not exempted. The role of human resource management functions becomes vital at such critical time. The study examines the gender differences in employee attrition and the perceptions on the talent management strategies at DUT. The study adopted a quantitative research approach and collected data from the university's database for the period 2018-2022. A descriptive data analysis using SPSS was conducted depicting the gender differences in the attrition rate at DUT. The findings of the study show that, (i) female employees are exiting the institution employment when compared to their counterpart, (ii) the institutions talent management strategies are work in progress. Furthermore, the study findings gave insight into an important practical implication needed by universities for an effective implementation of retention policies and practices especially in achieving the SDG of gender balancing. The study is limited to data collected within DUT and the findings cannot be used for generalization. The study is deemed important in addressing the increasing employee attrition rates and contributes to the on-going discourse in gender related issues facing higher education institution.

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Section: Employee Attrition and Talent Managementmentioning

confidence: 99%

Gender Differences in Employee Attrition at the Durban University of Technology

2023

ISEA

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“…There are many applications of those approaches in the literature that not only applied to solve HR problems [5] but also applied widely to solve business problems [4], healthcare problems [6], industry problems [7], etc. In the application of predictive analytics for HR, a conventional binary classification model like employee turnover prediction (ETP) is frequently studied in the literature to predict employee turnover [8]- [15]. A survival analysis model [16] may also be applied as predictive analytics to predict employee with the highest turnover probability at a given time.…”

Section: A Employee Turnovermentioning

confidence: 99%

“…1) Dataset 1: A synthetic HR dataset shared by Giri Pujar on Kaggle. This dataset consisting of 10 features with a sample of 14999 employees that previously studied by [8]- [11] using the ETP modelretrieved from https://www.kaggle.com/giripujar/hr-analytics.…”

Section: Human Resource Datasetsmentioning

confidence: 99%

Uplift modeling VS conventional predictive model: A reliable machine learning model to solve employee turnover

Wijaya

Barus

et al. 2021

Int. J. Artif. Intell. Res

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Employee turnover is the loss of talent in the workforce that can be costly for a company. Uplift modeling is one of the prescriptive methods in machine learning models that not only predict an outcome but also prescribe a solution. Recent studies are focusing on the conventional predictive models to predict employee turnover rather than uplift modeling. In this research, we analyze whether the uplifting model has better performance than the conventional predictive model in solving employee turnover. Performance comparison between the two methods was carried out by experimentation using two synthetic datasets and one real dataset. The results show that despite the conventional predictive model yields an average prediction accuracy of 84%; it only yields a success rate of 50% to target the right employee with a retention program on the three datasets. By contrast, the uplift model only yields an average accuracy of 67% but yields a consistent success rate of 100% in targeting the right employee with a retention program.

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Universiti Sains Malaysia (USM) Talent Management Dashboard

Mokhtar Alfakari,

Lutfi

2023

2023 6th International Conference on Applied Computational Intelligence in Information Systems (ACIIS)

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Machine Learning Approach for Employee Attrition Analysis

Cited by 12 publications

References 3 publications

Gender Differences in Employee Attrition at the Durban University of Technology

Gender Differences in Employee Attrition at the Durban University of Technology

Uplift modeling VS conventional predictive model: A reliable machine learning model to solve employee turnover

Universiti Sains Malaysia (USM) Talent Management Dashboard

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