A Study on Machine Learning Classifier Models in Analyzing Discipline of Individuals Based on Various Reasons Absenteeism from Work

The main purpose of this paper is to analyze the discipline failure nature of the employees based on various reasons absenteeism from work. This analysis plays a vital role in performance evaluation of the employees time to time and also in finding the rate of discipline failures in any organization; the organization can incorporate evolving better measures for human resource management, ultimately the effect of absenteeism on the productivity aspect could be reduced. The data was collected from UCI machine learning repository. The data was pre-processed in such a way that the attribute “discipline failure” is the class attribute. In this paper, we used five classifier models in analyzing the data. The performance of those models was also compared to find out better options of model for analysis. From the experimental results it is quite clear that the SMO and multilayer perception models are the best models suited for analysis with 100% accuracy.