UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 7 Issue 4
April-2020
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

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Published Paper ID:
JETIR2004460


Registration ID:
231240

Page Number

1900-1903

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Title

Survey: Employee Attrition Rate Prediction Using Machine Learning

Abstract

Every organization has its own productivity and strength which stands of the legs of the employees. Keeping regular employee is a great challenge for all organization in the competitive world. Employee Attrition is one of the biggest business problems in HR Analytics. Companies invest a lot in the training of the employees keeping in mind the returns they would provide to the company in the future. If an employee leaves the company, it is the loss of opportunity cost to the company. These study interpreters the employee’s attrition rate through the related attributes like Job Role, overtime, job level affect the attrition largely. The paper contain the survey of various classification algorithms like logistic regression, LDA, ridge classification, decision trees, random forests to predict the probability of attrition of any new employee.

Key Words

Attrition Rate, HR, Classifier, Preprocessing, Employment Features.

Cite This Article

"Survey: Employee Attrition Rate Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.1900-1903, April-2020, Available :http://www.jetir.org/papers/JETIR2004460.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Survey: Employee Attrition Rate Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp1900-1903, April-2020, Available at : http://www.jetir.org/papers/JETIR2004460.pdf

Publication Details

Published Paper ID: JETIR2004460
Registration ID: 231240
Published In: Volume 7 | Issue 4 | Year April-2020
DOI (Digital Object Identifier):
Page No: 1900-1903
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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