dc.contributor.author |
Gamage, Thinayani |
|
dc.date.accessioned |
2024-03-13T05:18:56Z |
|
dc.date.available |
2024-03-13T05:18:56Z |
|
dc.date.issued |
2023 |
|
dc.identifier.citation |
Gamage, Thinayani (2023) Ear-sys - an Ensemble Approach Using Super Learner for Employee Attrition Prediction and Retention System. BSc. Dissertation, Informatics Institute of Technology |
en_US |
dc.identifier.issn |
2019714 |
|
dc.identifier.uri |
http://dlib.iit.ac.lk/xmlui/handle/123456789/1875 |
|
dc.description.abstract |
"Employee attrition has become a major concern for organizations, and retaining employees has become crucial for their success. This project proposes an ensemble approach using the Super Learner technique, EAR-Sys, for employee attrition prediction and retention system. This project aims to develop a model that predicts employee attrition with high accuracy and provides insights for retention strategies.
This project demonstrates the effectiveness of the Super Learner approach in employee attrition prediction and retention. The findings contribute to the body of knowledge by providing insights into the factors that influence employee attrition and retention. The proposed model has practical implications for HR managers and can help organizations to retain their valuable employees and achieve their strategic goals.
" |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IIT |
en_US |
dc.subject |
Employee Attrition |
en_US |
dc.subject |
Employee Churn |
en_US |
dc.subject |
Machine Learning |
en_US |
dc.subject |
Retention Strategies |
en_US |
dc.title |
Ear-sys - an Ensemble Approach Using Super Learner for Employee Attrition Prediction and Retention System |
en_US |
dc.type |
Thesis |
en_US |