Machine Learning and Artificial Intelligence Inform EV Adoption Levels
One of the largest challenges with managing large datasets is ensuring they are complete. Many use cases can use machine learning (ML) and artificial intelligence (AI) algorithms to accurately identify and fill gaps of unknown historical data sets with data extrapolated from other data sources. We recently put this to the test by analyzing electric vehicle (EV) adoption levels in the U.S. and identifying which states and regions have (or will have) high and low EV adoption. See how Altair’s data analytics solutions rose to the challenge.
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Electric Vehicle Adoption
Interactive Map
With EV adoption data available for only 15 states, Altair’s predictive data modeling technology applied machine learning to impute the missing data to show complete EV adoption levels across the U.S.
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