How it works...
We begin by reading in our dataset and then standardizing it, as in the recipe on standardizing data (steps 1 and 2). (It is necessary to work with standardized data before applying PCA). We now instantiate a new PCA transformer instance, and use it to both learn the transformation (fit) and also apply the transform to the dataset, using fit_transform (step 3). In step 4, we analyze our transformation. In particular, note that the elements of pca.explained_variance_ratio_ indicate how much of the variance is accounted for in each direction. The sum is 1, indicating that all the variance is accounted for if we consider the full space in which the data lives. However, just by taking the first few directions, we can account for a large portion of the variance, while limiting our dimensionality. In our example, the first 40 directions account for 90% of the variance:
sum(pca.explained_variance_ratio_[0:40])
This produces the following output:
This means that we can reduce our number of features to 40 (from 78) while preserving 90% of the variance. The implications of this are that many of the features of the PE header are closely correlated, which is understandable, as they are not designed to be independent.