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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Employee Attrition System: Using Machine Learning to Evaluate Performance of the Staff |
| Authors Name: | Asiya Anjum |
| Unique Id: | IJSDR2211015 |
| Published In: | Volume 7 Issue 11, November-2022 |
| Abstract: | Employee’s attrition prediction has recently become a major issue in organizations. Employee turnover is a notable problem for organizations, particularly when highly qualified, technical, and key employees leave for better opportunities. This results in a loss of income because a trained employee must be replaced. As a result, we evaluate the common reasons for employee attrition using recent and historical employee data. Methods for supervising machine learning are described, demonstrated, and implemented. Evaluated for predicting employee turnover within an organization In this study, numerical experiments for real and simulated human resources datasets representing organizations with small, medium, and large employee populations are performed on the human resource data using Decision Tree, Logistic Regression, SVM, KNN, Random Forest, and Naive Bayes methods. To prevent employee attrition, we apply the feature selection method to the data and analyze the results. This helps companies predict employee attrition and helps their economic growth by lowering human resource costs. |
| Keywords: | — Employee, Opportunities, Predicting, Decision Tree, Logistic Regression, SVM, KNN, Random Forest, and Naive Bayes methods, Machine Learning, Cost, Data mining, Data base, Data Science. |
| Cite Article: | "Employee Attrition System: Using Machine Learning to Evaluate Performance of the Staff ", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 11, page no.83 - 95, November-2022, Available :http://www.ijsdr.org/papers/IJSDR2211015.pdf |
| Downloads: | 000337212 |
| Publication Details: | Published Paper ID: IJSDR2211015 Registration ID:202441 Published In: Volume 7 Issue 11, November-2022 DOI (Digital Object Identifier): Page No: 83 - 95 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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