The Application of Machine Learning Algorithms in Predicting the Usage of IoT-based Cleaning Dispensers: Machine Learning Algorithms in Predicting the Usage if IoT-based Dispensers

Abstract

Internet of Things (IoT) based liquid cleaning dispensers are being increasingly used in public buildings for personal sanitation purposes. However, it is not always easy for facility managers to keep track of, as well as predict product usage. Most devices deployed at facilities still require the facility/building manager or staff at the facility, to check the devices from time to time. In recent years, the need to effectively utilize these devices as well as anticipate usage rates has become necessary because the time lag between refilling the dispensers and their being out of service can pose health risks. This paper thus explores how machine learning (ML) algorithms can be applied to improve the availability of IoT-based liquid cleaning dispensers. The goal of the paper is to apply machine learning in predicting the daily usage volumes of cleaning solutions, thereby increasing the efficiency of the cleaning dispensers. The paper compares different machine learning algorithms to determine the best algorithm for predicting the usage patterns of IoT-based cleaning dispensers, thereby, develops a predictive model that can be applied to improve the availability of cleaning products in IoT-based dispensers. The results of the analysis show that the Random Forest algorithm performed best among the evaluated models using regression performance measures. Hence, ML algorithms can be applied to help building or sanitation managers improve the availability of cleaning products in IoT-based cleaning dispensers, ultimately improving the user experience.