Managing employee turnover: machine learning to the rescue

2021 ◽  
Vol 6 (1) ◽  
pp. 57
Author(s):  
Owen P. Hall
Keyword(s):  
Machine Learning ◽  
Employee Turnover

scholarly journals An introductory study to machine learning and its application to employee turnover prediction

2019 ◽  
Author(s):  
João Pedro Pazinato Cruz de Oliveira ◽  
Leonardo Tomazeli Duarte
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Test Data ◽  
Employee Turnover ◽  
Learning Machine ◽  
Elm Classifier

The objective of this paper is to study the problem of employee turnover prediction and to develop a classifier that uses employee's data to identify those who have a greater tendency to leave the company voluntarily. For such purpose, the data of 8724 employees from a real Brazilian beverage company was used to train an Extreme Learning Machine (ELM) classifier, assigning to each sample a weight inversely proportional to the size of the respective class. After the training, the classifier displayed an overall accuracy of 79% of the test data.

Predicting Job Satisfaction and Employee Turnover Using Machine Learning

2020 ◽  
Vol 17 (9) ◽  
pp. 4092-4097
Author(s):  
Inchara Yogesh ◽  
K. R. Suresh Kumar ◽  
Niveditha Candrashekaran ◽  
Dhrithi Reddy ◽  
Harshitha Sampath
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Succession Planning ◽  
Employee Turnover ◽  
Employee Retention ◽  
Random Forest Classifier ◽  
Machine Learning Techniques ◽  
Positive Feedbacks ◽  
Learning Techniques ◽  
New Employee

Employee turn_over inflicts costs on the company. The employee must be supplanted, and the new employee trained. These quits may likewise make critical and exorbitant interruptions the production process. This gives lucid motivation to the firm to forestall stops or, in any event, to have the option to anticipate when and where stops can be anticipated. On the off chance that employees are approached to assess their superiors and the appropriate responses will be made accessible to the superior, it is most obvious that only positive feedbacks will be provided. Along these lines, the point is to utilize Machine Learning techniques to foresee employee turn_over. Appropriate predictions cause companies to take necessary decisions on employee retention or succession planning. Algorithms: One-Sample T-Test (T-Test), Decision Tree (DT), AdaBoost (AB), Logistic Regression (LR), Random Forest Classifier (RFC).

Employee Turnover Prediction with Machine Learning: A Reliable Approach

2018 ◽  
pp. 737-758 ◽  
Author(s):  
Yue Zhao ◽  
Maciej K. Hryniewicki ◽  
Francesca Cheng ◽  
Boyang Fu ◽  
Xiaoyu Zhu
Keyword(s):  
Machine Learning ◽  
Employee Turnover

scholarly journals Foreseeing Employee Attritions using Diverse Data Mining Strategies

2019 ◽  
Vol 8 (3) ◽  
pp. 620-626
Keyword(s):  
Machine Learning ◽  
Best Practices ◽  
Employee Turnover ◽  
Employee Retention ◽  
Forecast Model ◽  
Learning Systems ◽  
Knowledge Based ◽  
Machine Learning Model ◽  
Employee Attrition ◽  
Diverse Data

“Employee turnover is a noteworthy matter in knowledge-based companies.” On the off chance that employee leaves, they carry with them tacit information, often a source of competitive benefit to the other firms. Keeping in mind the end goal, to stay in the market and retain its employees, an organization requires minimizing employee attrition. This article discusses the employee churn/attrition forecast model using various methods of Machine Learning. Model yields are then scrutinized to outline and experiment the best practices on employee withholding at different stages of the employee’s association with an organization. This work has the potential for outlining better employee retention designs and enhancing employee contentment. This paper incorporates and condenses the capacity to gain from information and give information-driven experiences, choice, and forecasts and thinks about significant machine learning systems that have been utilized to create predictive churn models.

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