So far, we've been limiting ourselves to two-dimensional data. After all, the human mind has a lot of trouble dealing with more than three dimensions, and even two-dimensional visualizations of three-dimensional space can be difficult to comprehend.
But we can use principal component analysis (PCA) to project higher-dimensional data down into lower dimensions and still capture the most significant relationships in the data. It does this by re-projecting the data onto a lower dimension in a way that captures the maximum amount of variance in the data. This makes the data easier to visualize in three- or two-dimensional space, and it also provides a way to select the most relevant features in a dataset.
In this recipe, we'll take the US Census race data that we've worked with in previous chapters and create a two-dimensional scatter plot of it.