Employee Attrition Prediction Using Classification Models

Bhartiya, Namrata; Jannu, Sheetal; Shukla, Purvika; Chapaneri, Radhika

doi:10.1109/i2ct45611.2019.9033784

Cited by 17 publications

(7 citation statements)

References 5 publications

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“…In another study [46], the authors focused on systematically predicting attrition using Machine Learning and Data Analysis methods. They utilized the IBM HR Employee Analytics Attrition and Performance dataset.…”

Section: 21machine Learningmentioning

confidence: 99%

Employee Attrition Prediction using Machine Learning Models: A Review Paper

Alqahtani,

Almagrabi,

Alharbi

2024

IJAIA

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Employee attrition refers to the decrease in staff numbers within an organization due to various reasons. As it has a negative impact on long-term growth objectives and workplace productivity, firms have recognized it as a significant concern. To address this issue, organizations are increasingly turning to machine-learning approaches to forecast employee attrition rates. This topic has gained significant attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims to fill this gap by providing an overview of research conducted on applying machine learning to predict employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted, summarized, and classified. Most studies agree on conducting comparative experiments with multiple predictive models to determine the most effective one.From this literature survey, the RF algorithm and XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms. Additionally, the application of deep learning to employee attrition prediction issues also shows promise. While there are discrepancies in the datasets used in previous studies, it is notable that the dataset provided by IBM is the most widely utilized. This study serves as a concise review for new researchers, facilitating their understanding of the primary techniques employed in predicting employee attrition and highlighting recent research trends in this field. Furthermore, it provides organizations with insight into the prominent factors affecting employee attrition, as identified by studies, enabling them to implement solutions aimed at reducing attrition rates.

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Section: 21machine Learningmentioning

confidence: 99%

Employee Attrition Prediction using Machine Learning Models: A Review Paper

Alqahtani,

Almagrabi,

Alharbi

2024

IJAIA

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“…The systematic flow for predicting employee attrition using machine learning techniques was proposed in this research study [24]. The machine learning models Naive Bayes, Random Forest, Decision Tree, Support Vector Machine, and K-Nearest Neighbor were applied using the python tool.…”

Section: Related Workmentioning

confidence: 99%

Predicting Employee Attrition Using Machine Learning Approaches

et al. 2022

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Employee attrition refers to the natural reduction in the employees in an organization due to many unavoidable factors. Employee attrition results in a massive loss for an organization. The Society for Human Resource Management (SHRM) determines that USD 4129 is the average cost-per-hire for a new employee. According to recent stats, 57.3% is the attrition rate in the year 2021. A research study needs to be implemented to find the causes of employee attrition and a learning framework to predict employee attrition. This research study aimed to analyze the organizational factors that caused employee attrition and the prediction of employee attrition using machine learning techniques. The four machine learning techniques were applied in comparison. The proposed optimized Extra Trees Classifier (ETC) approach achieved an accuracy score of 93% for employee attrition prediction. The proposed approach outperformed recent state-of-the-art studies. The Employee Exploratory Data Analysis (EEDA) was applied to determine the factors that caused employee attrition. Our study revealed that the monthly income, hourly rate, job level, and age are the key factors that cause employee attrition. Our proposed approach and research findings help organizations overcome employee attrition by improving the factors that cause attrition.

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“…Attributes like Gender, Education Field, and Performance Rate were visualized for Attrition parameters thus giving an idea on the relevant features. A comparison between the performance metrics of the classification models provided new insights on improving the work ethics [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Churn Prediction of Employees Using Machine Learning Techniques

Bandyopadhyay

Jadhav

2021

Teh. glas. (Online)

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Employees are considered as the most valuable assets of any organization. Various policies have been introduced by the HR professionals to create a good working environment for them, but still, the rate of employees quitting the Technology Industry is quite high. Often the reason behind their early attrition could be due to company-related or personal issues, such as No satisfaction at the workplace, Fewer opportunities for learning, Undue Workload, Less Encouragement, and many others. This paper aims in discussing a structured way for predicting the churn rate of the employees by implementing various Classification techniques like SVM, Random Forest classifier, and Naives Bayes classifier. The performance of the classifiers was compared using metrics like Confusion Matrix, Recall, False Positive Rate, and Accuracy to determine the best model for the churn prediction. We found that among the models, the Random Forest classifier proved to be the best model for IT employee churn prediction. A Correlation Matrix was generated in the form of a heatmap to identify the important features that might impact the attrition rate.

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Employee Attrition Prediction Using Classification Models

Cited by 17 publications

References 5 publications

Employee Attrition Prediction using Machine Learning Models: A Review Paper

Employee Attrition Prediction using Machine Learning Models: A Review Paper

Predicting Employee Attrition Using Machine Learning Approaches

Churn Prediction of Employees Using Machine Learning Techniques

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