Employee Absenteeism Prediction Using Machine Learning
Pradeep Kumar Kushwaha, Ajay Rana, Swapnil Srivastava, Aamir Saifi, Aryan Tavish, Prateek Chaturvedi
Abstract
This paper presents a comprehensive review of the current state-of-the-art in employee absenteeism prediction using machine learning techniques. The paper begins by providing an overview of the various factors that contribute to absenteeism, including employee demographics, job characteristics, organizational factors, and personal factors. It then discusses the different types of data that can be used to predict absenteeism, such as attendance records, performance data, and survey data. Next, the paper reviews various machine learning techniques that have been applied to predict employee absenteeism, including decision trees, logistic regression, support vector machines, and neural networks [2]. Each technique is described in detail, along with its advantages and limitations. The paper also discusses the importance of feature selection and data preprocessing in improving the accuracy of absenteeism prediction models. Finally, the paper discusses the challenges and limitations of using machine learning techniques for employee absenteeism prediction. These include data privacy concerns, the need for high-quality data, and the potential for bias in the models [4]. The paper concludes with recommendations for organizations considering implementing a machine learning-based absenteeism prediction system, including the need for a clear business case, stakeholder buy-in, and a plan for monitoring and evaluating the system's effectiveness [5].