Stop 08 · Unsupervised LearningTopics · 4/7
Principal Component Analysis (PCA)
Part of the Data Science Roadmap
In short
A technique that transforms many correlated features into fewer uncorrelated components, ranked by how much variance they explain — used for dimensionality reduction and visualization.
How to learn this
- 1Apply PCA to a high-dimensional dataset and plot the first 2 components.
- 2Learn to interpret an explained-variance plot to decide how many components to keep.
- 3Understand PCA components lose direct interpretability — a real trade-off to weigh.
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