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Absenteeism prediction in Call Center using machine learning algorithms
Absenteeism is a major problem faced particularly by companies with a large number of employees. Therefore, the existence of absenteeism prediction tools is essential for such companies depending on intensive human-resources. This paper focuses on using machine learning technologies for predicting the absences of employees from work. More precisely, a few prediction models were tuned and tested with 241 features extracted from a population of 13.805 employees. This target population was sampled from the help desk work force of a major Brazilian phone company. The features were extracted from the profile of the help desk agents and then filtered by processes of correlation and feature selection. The selected features were then used to compare absenteeism prediction given by diferent classification algorithm (cf. Random Forest, Multilayer Perceptron, Support Vector Machine, Naive Bayes, XGBoost and Long Short Term Memory). The parameterization of these ML models was also studied to reach the classifier best suited for the prediction problem. Such parameterizations were tuned through the use of evolutionary algorithms, from which considerable precision was reached, the best being 72% (XGBoost) and 71% (Random Forest).