UGC Approved Journal no 63975(19)

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

Volume 9 Issue 8
August-2022
eISSN: 2349-5162

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

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Unique Identifier

Published Paper ID:
JETIRFP06069


Registration ID:
500150

Page Number

400-404

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Title

Accessing the Organisation by Predicting Employee Attrition Rate

Abstract

Employees are valuable assets of any organization. But if they quit jobs unexpectedly, it may incur a huge cost to any organization. Employee attrition is a reduction in manpower in any organization where employees may voluntarily leave the organization, so designing a model that helps to predict attrition rate and understand the key variables that influence the employee attrition rate using machine learning. Organizations face huge cost resulting from employee turnover. It may incur a high cost such as training expenses and the time it takes from when an employee starts to when they become a productive member. When a productive employee quits the organization, it will loss new ideas, great project management. To overcome this, a system is design for finding the hidden reasons for employees’ attrition can be identified, and management can take preventive actions regarding attrition of each employee. To implement the proposed model, The data is downloaded from Kaggle is preprocessed to extract the features that are quite natural for employee attrition, and becomes as input to the model. The main attributes are total working years, salary hike, satisfaction level. Using dependent variable, we reached to the factors that are mostly dependent on employee related variables. So here by using the dataset the model achieves the terms or the factor that are mostly affect the employee attrition rate. The analyzed data is visualized for word to vector formation and on this fined tuned data we can apply algorithm to get the final result. Using Random Forest algorithm, the proposed system is performing well in predicting the employee, those are likely to quit the respective organization based on their working details.

Key Words

Attrition Rate, Classifier, Random Forest algorithm, Organisation, Employee

Cite This Article

"Accessing the Organisation by Predicting Employee Attrition Rate", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.400-404, August-2022, Available :http://www.jetir.org/papers/JETIRFP06069.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

"Accessing the Organisation by Predicting Employee Attrition Rate", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. pp400-404, August-2022, Available at : http://www.jetir.org/papers/JETIRFP06069.pdf

Publication Details

Published Paper ID: JETIRFP06069
Registration ID: 500150
Published In: Volume 9 | Issue 8 | Year August-2022
DOI (Digital Object Identifier):
Page No: 400-404
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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