Introduction
In this chapter, we will dive more into Exploratory Data Analysis (EDA). This is the process of sifting through the data and trying to make sense of the individual columns and the relationships between them.
This activity can be time-consuming, but can also have big payoffs. The better you understand the data, the more you can take advantage of it. If you intend to make machine learning models, having insight into the data can lead to more performant models and understanding why predications are made.
We are going to use a dataset from www.fueleconomy.gov that provides information about makes and models of cars from 1984 through 2018. Using EDA we will explore many of the columns and relationships found in this data.
