Abstract
For a growing class of prediction problems, big data and machine learning (ML) analyses can greatly enhance our understanding of the effectiveness of public investments and public policy. However, the outputs of many ML models are often abstract and inaccessible to policy communities or the general public. In this article, we describe a hands-on teaching case that is suitable for use in a graduate or advanced undergraduate public policy, public affairs, or environmental studies classroom. Students will engage on the use of increasingly popular ML classification algorithms and cloud-based data visualization tools to support policy and planning on the theme of electric vehicle mobility and connected infrastructure. By using these tools, students will critically evaluate and convert large and complex data sets into human understandable visualization for communication and decision making. The tools also enable user flexibility to engage with streaming data sources in a new creative design with little technical background.
Published Version
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