Employee Attrition System: Using Machine Learning to Evaluate Performance of the Staff
— Employee, Opportunities, Predicting, Decision Tree, Logistic Regression, SVM, KNN, Random Forest, and Naive Bayes methods, Machine Learning, Cost, Data mining, Data base, Data Science.
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.
"Employee Attrition System: Using Machine Learning to Evaluate Performance of the Staff ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 11, page no.83 - 95, November-2022, Available :https://ijsdr.org/papers/IJSDR2211015.pdf
Volume 7
Issue 11,
November-2022
Pages : 83 - 95
Paper Reg. ID: IJSDR_202441
Published Paper Id: IJSDR2211015
Downloads: 000347168
Research Area: Computer Science & Technology
Country: Hyderabad, Telangana, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
