Predicting and Analyzing Absenteeism at Workplace Using Machine Learning Algorithms

Absenteeism is the usual or recurrent absence from work is continuously causing disruption in the smooth running of business, affecting the organizational performance and productivity and impacting on the employees’ morale. The Oil Refinery in Albania (ARMO), employing 1200 employees is facing high rate of absences. If necessary measures are not being serious dealt with, the issue of absenteeism may jeopardize the operation and production. Prediction of absenteeism is too complex influenced by many factors. Usage of data mining and machine learning algorithms is a good solution to predict and analyze it. The aim of this paper is to identify and evaluate the appropriate ML algorithms to predict and analyses absenteeism at workplace. The dataset taken into account consists of some attributes such as: age, education, employment category, day, month, length of service ect, and 125000 records are considered. Analysis and comparison of various algorithms in terms of accuracy, precision and sensitivity are done in Weka tool.