PCA

Principal Component Analysis (PCA) attempts to reduce the number of features within a dataset while retaining the “principal components”, which are defined as weighted combinations of existing features that:

1. Are uncorrelated with one another, so you can treat them as independent features, and
2. Account for the largest possible variability in the data!

So, depending on how many components we want to produce, the first one will be responsible for the largest variability on our data and the second component for the second-most variability, and so on. Which is exactly what we want to have for clustering purposes!

PCA is commonly used when you have data with many many features.

You can learn more about the details of the PCA algorithm in the video, below.

Now, in our case, we have data that has 34-dimensions and we’ll want to use PCA to find combinations of features that produce the most variability in the population dataset.

The idea is that components that cause a larger variance will help us to better differentiate between data points and (therefore) better separate data into clusters.

So, next, I’ll go over how to use SageMaker’s built-in PCA model to analyze our data.