Software employee promotion analysis using machine learning

Registration ID: IJNRD_192223

Published ID: IJNRD2305477

DOI: Click Here to Get

G.Govardhan Reddy , B.Pavan kumar , T.R Harish , K.Rajendra , Syed Abuthahir

employee promotion, prediction, HR dataset, data management, RF, SVM, GTC

Employee attrition is the term used to describe the organic decline in the number of employees in a company as a result of several unavoidable circumstances. Employee churn causes a significant loss or an organization, a loss. According to the Society for Human Resource Management (SHRM), that is the typical cost per hire for a new hire. Recent statistics indicate that the attrition rate in 2021 will be 57.3%. The accuracy scores obtained using the deployed machine learning approaches were 87% by SVM methodology, and 93% overall. This project is focused on gathering information on employees, creating a decision tree using historical data, testing the decision tree using an employee's traits, and determining whether to provide a promotion or not. The trained dataset kept in the decision tree is compared to this data. Identifying is the ultimate objective node. The suggested improved Decision Trees Classifier (DTC) predicts whether the employee will receive a yearly raise or promotion or not. the technique produced predictions of staff attrition that were up to 96% accurate.

Published Paper   E-Certificate

G.Govardhan Reddy, B.Pavan kumar, T.R Harish, K.Rajendra, & Syed Abuthahir (May-2023). Software employee promotion analysis using machine learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), e602-e606. https://ijnrd.org/papers/IJNRD2305477.pdf

Volume 8 Issue 5, May-2023

Pages : e602-e606

Paper Reg. ID: IJNRD_192223

Published Paper Id: IJNRD2305477

Downloads: 000121975

Research Area: Computer Engineering 

Country: anamaya, Andhra Pradesh , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305477.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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