Which features carry the most weight? How do the original features contribute to the principal components? These 5 types of visualization have the answer.
Principal component analysis (PCA) can tell you a lot about your data. Simply put, it is a dimensionality reduction technique used to fit high-dimensional data sets into a visualizable space.
But I guess you already know that. If not, check out my guide from scratch.
Today we will focus solely on the visual aspect. By the end of the article you will know how to create and interpret:
- Explained variance graph
- Cumulative explained variance graph
- 2D/3D component scatter plot
- Attribute biplot diagram
- Load Score Chart
I'd love to dive into visualizations right away, but you'll need data to follow along. This section covers data loading, preprocessing, PCA fitting, and general Matplotlib style settings.
