Linear Attribute Projection and Performance Assessment for Signifying the Absenteeism at Work using Machine Learning
M. Shyamala Devi1, Usha Vudatha2, Sukriti Mukherjee3, Bhavya Reddy Donthiri4, S B Adhiyan5, Nallareddy Jishnu6
1M. Shyamala Devi, Associate Professor, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, TamilNadu, India.
2Usha Vudatha, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, TamilNadu, India.
3Sukriti Mukherjee, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, TamilNadu, India.
4Bhavya Reddy Donthiri, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, (Tamil Nadu) India.
5S B Adhiyan, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, (Tamil Nadu) India.
6Nallareddy Jishnu, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, (Tamil Nadu) India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 60-68 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4405098319/19©BEIESP | DOI: 10.35940/ijrte.C4405.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In recent times, with the technological advancement the industry and organization are transforming all their inflow and outflow operations into digital identity. At the outset, the name of the organization is also in the hands of the employee. One of the major needs of the employee in the working environment is to avail leave or vacation based on their family circumstances. Based on the health condition and need of the employee, the organization must extend their leave for the satisfaction of the employee. The performance of the employee is also predicted based on the working days in the organization. With this view, this paper attempts to analyze the performance of the employee and the number of working hours by using machine learning algorithms. The Absenteeism at work dataset from UCI machine learning Repository is used for prediction analysis. The prediction of absent hours is achieved in three ways. Firstly, the correlation between each of the dataset attributes are found and depicted as a histogram. Secondly, the top most high correlated features are identified which are directly fitted to the regression models like Linear regression, SRD regression, RANSAC regression, Ridge regression, Huber regression, ARD Regression, Passive Aggressive Regression and Theilson Regression. Thirdly, the Performance analysis is done by analyzing the performance metrics like Mean Squared Error, Mean Absolute Error, R2 Score, Explained Variance Score and Mean Squared Log Error. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Result shows that the Passive Aggressive Regression have achieved the effective prediction of number of absent hours with minimum MSE of 0.04, MAE of 0.16, EVS of 0.03, MSLE of 0.32 and reasonable R2 Score of 0.89.
Index Terms: Machine Learning, MSE, MAE, R2 Score, Explained Variance Score and Mean Squared Log Error.
Scope of the Article: High Performance Computing