Exploratory data analysis is part and parcel of any model-building process. Understanding the algorithm at play, too, is important. Given that this chapter revolves around linear regression, it might be worth it to explore the data through the lens of understanding linear regression.
But first, let's look at the data. One of the first things I recommend any budding data scientist keen on machine learning to do is to explore the data, or a subset of it, to get a feel for it. I usually do it in a spreadsheet application such as Excel or Google Sheets. I then try to understand, in human ways, the meaning of the data.
This dataset comes with a description of fields, which I can't enumerate in full here. A snapshot, however, ...
