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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM1815

Employee Attrition Prediction Using Machine Learning Tool PYTHON JUPITER

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM1815

Volume:

3 |

8 |

Aug 2019

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

GH

Giriraj H

KVV

Kashinath V Vernekar

MKB

MAHENDRA KUMAR B

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Abstract

Employee attrition is a gradual but intentional reduction in the number of employees in a company or business organization. Employees will at some point in time look to change their job places for a number of reasons. It might be for professional or personal reasons but it does happen. But it is a diminishing in the number of the workforce due to several factors that are very avoidable. Some forms of attrition are unavoidable, like if an employee is retiring or is moving to another city. But when attrition crosses a particular threshold, it becomes a cause for concern. For example, attrition among minority employee groups could be hurting diversity at your organization. Or, attrition among senior leaders can lead to a significant gap in organizational leadership. Do you know where your company stands on the employee attrition curve? The objective is to analyse the factors that affect employee attrition using previous year’s employee’s historical data and predict chance of the current employee, whether he could leave the organization. A model is proposed along with an algorithm to predict the chance of employee attrition.

KEYWORDS

KNN / Random Forest / SVM

Cite This Article

Giriraj H, Kashinath V Vernekar, MAHENDRA KUMAR B, "Employee Attrition Prediction Using Machine Learning Tool PYTHON JUPITER," International Journal of Scientific Research in Engineering and Management, vol. 3, no. 8, Aug 2019.

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Publication Date

3/10/2023

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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