Employee Attrition Prediction
Benson Antony D V1, Haritha Rajeev2
1Benson Antony D V, Department of Computer Science, St. Albert’s College, Kochi (Kerala), India.
2Haritha Rajeev, Department of Computer Science, St. Albert’s College, Kochi (Kerala), India.
Manuscript received on 24 April 2024 | Revised Manuscript received on 14 May 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 May 2024 | PP: 26-30 | Volume-4 Issue-1 May 2024 | Retrieval Number: 100.1/ijdm.A163604010524 | DOI: 10.54105/ijdm.A1636.04010524
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Employee attrition occurs when a worker leaves a company to join another firm for a better offer. It might also be referred to as Employee Defection. Representative downsizing is likely to be significant when there is a pressing demand for workers in a particular industry due to mass retirements or organizational growth. At one point, the programming industry had significant attrition rates due to abundant job opportunities in the software sector driven by the demand for software products across all industries. Reducing the employee attrition rate is a challenging challenge faced by HR managers. This study provides a clear viewpoint on predicting employee turnover using Machine Learning methods. The projection is completed using data obtained from IBM HR analysis. We employed Logistic Regression for the analysis and achieved an accuracy rate of 87%.
Keywords: Employee Turnover, Human Resources Managers, Logistic Regression, Machine Learning Model, Software Development Sector.
Article of the Scope: Data Science