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Here are the files you saw in the last video. Use them to help you answer the following questions.
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Learn to clean up messy data, uncover patterns and insights, make predictions using machine learning, and clearly communicate your findings.
Cleaning Column Labels 1. Drop extraneous columns Drop features that aren't consistent (not present in both datasets) or aren't relevant to our questions. Use pandas' drop function. Columns to Drop: From 2008 dataset: 'Stnd', 'Underhood ID', 'FE Cal...
Assessing Data The files all_alpha_08.csv and all_alpha_18.csv discussed in the previous pages have been provided in the workspace for you here to access. Use pandas to explore these datasets in the Jupyter Notebook below to answer the quiz questions...
Fuel Economy Data This information is provided by the U.S. Environmental Protection Agency, Office of Mobile Sources, National Vehicle and Fuel Emissions Laboratory. ^ here
yt link Data Overview We'll learn more about these features as we go. Right now, we have a good idea of what data we're working with, so let's answer some questions. You'll see quick descriptions of each feature again in the next section to help you....