This guide presents a data-driven study that uses machine learning and visualization to estimate electric vehicle adoption across U.S. counties, including regions with incomplete registration data. It explains how Altair RapidMiner and Panopticon were used to impute missing values, identify high-impact predictors, and build predictive models using decision trees, logistic regression, and random forest algorithms. The guide details data sources, feature selection, model validation, and visualization techniques, including choropleth maps and adoption heatmaps. Results reveal unexpected adoption hotspots and demonstrate how socioeconomic factors, infrastructure availability, and policy indicators influence EV adoption. The guide illustrates how advanced analytics can inform policy, infrastruc

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