Predicting Customer Quality of Service for a Large Fixed Broadband Service Provider.

The number of fixed broadband customers has continued to increase over the past decade in Brazil. In spite of its demand, customers face numerous issues with broadband services. Thus, in this paper, we partner with TIM, one of the largest fixed broadband service providers in Brazil, to analyze customers' Quality of Service (QoS) parameters and predict customers' download rates. We consider 3.4 million logs collected from 5% of the total customers for 31 days starting from May 22nd, 2021. We build a framework using Error-Correcting Output Codes (ECOC) and H2O's Automatic Machine Learning (AutoML) that accurately predicts the quality of service, particularly the download rate, achieved by the customers using features related to customer location, internet plan, and equipment. Our experiments demonstrate that our model achieves around 83% accuracy on average on our dataset. Our framework can be used by TIM to improve its fixed broadband services.