Predicting and visualizing EV adoption with Rapidminer
Build a data science-ready architecture that scales flexibly and promotes data fluency across your organization.
Data drives vital elements of our society and the ability to capture, interpret and leverage critical data is one of Siemens’ core differentiators. While Siemens’ data analytics tools are applied to complex problems involving manufacturing efficiency, product design, process automation and securities trading, they’re also useful in a variety of more common business intelligence applications, too.
A Siemens team undertook a project utilizing Siemens machine learning (ML) software and data visualization tools to investigate a newsworthy topic of interest today: the adoption level of electric vehicles, including both BEVs and PHEVs, in the United States at the county level.
This eBook explains the team’s findings and the process they used to arrive at their conclusions.