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A Survey on Predicting Employee's Performance

Authors: Komal Vikas Kaware; Pratiksha Maruti Viveki; Shrutika Dnyanesh Lokhande; Avadhut, Nikita Neminath; Vaishnavi Somnath Pattanshetti; S. A. Shinde;

A Survey on Predicting Employee's Performance

Abstract

{"references": ["Kirimi JM, Motur CA. Application of Data Mining Classification in Employee Performance Prediction. International Journal of Computer Applications, July 2016. 146.(7)", "Desouki M. S., Al-Daher J. Using Data Mining Tools to Improve the Performance Appraisal Procedure, HIAST Case. International Journal of Advanced Information Arts, Science & Management. February 2015.2(1).", "V. Kalaivani, Mr. M. Elamparithi. An Efficient Classification Algorithms for Employee Performance Prediction. International Journal of Research in Advent Technology, September 2014.2(9). E.ISSN: 2321-9637.", "Hamidah Jantan, Norazmah Mat Yusoff and Mohamad Rozuan Noh. Towards Applying Support Vector Machine Algorithm in Employee Achievement Classification. Proceedings of the International Conference on Data Mining, Internet Computing, and Big Data, Kuala Lumpur, Malaysia, 2014 ISBN: 978- 1-941968-02-4 \u00a92014 SDIWC.", "Lipsa Sadath. Data Mining: A Tool for Knowledge Management in Human Resource. International Journal of Innovative Technology and Exploring Engineering (IJITEE), April 2013.2.", "https://www.ieeexplorer.org/", "https://www.kaggle.com/", "https://www.wikipedia.com/"]}

The major goal of this project is to predicting the employee’s performance using machine learning algorithms for data mining. Data Mining is used for information discovery of interest in Human Resources Management (HRM). Employees are evaluated from their performance. For this we use different methods such as Decision Tree, SVM, and Feature Selection etc. This System enhances the performance of employee’s.

Keywords

Decision Tree, Feature Selection, HRM, MongoDb, SVM., http://hbrppublication.com/journals.html

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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