I wrote a blog explaining PCA from scratch — math, worked example, and Python implementation

PCA is one of those topics where most explanations either skip the math entirely or throw equations at you without any intuition.

I tried to find the middle ground.

The blog covers:

• Variance, covariance, and eigenvectors
• A full worked example with a dummy dataset
• Why we use the covariance matrix specifically
• Python implementation using sklearn
• When PCA works and when it doesn't

No handwaving. No black boxes.

The blog link is: Medium

Happy to answer any questions or take feedback in the comments.