Which features carry the most weight? How do original features contribute to principal components? These 5 visualization types have the answer.
Principal Component Analysis (PCA) can tell you a lot about your data. In short, it’s a dimensionality reduction technique used to bring high-dimensional datasets into a space that can be visualized.
But I assume you already know that. If not, check my from-scratch guide.
Today, we only care about the visuals. By the end of the article, you’ll know how to create and interpret:
- Explained variance plot
- Cumulative explained variance plot
- 2D/3D component scatter plot
- Attribute biplot
- Loading score plot
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 styling tweaks.