Imagine patient data collected in a hospital. Why do we need PCA?Ī lot of data is high-dimensional. Although PCA is most often applied to find a lower-dimensional representation of data, it can also be used for other purposes, e.g. It has been around for more than 100 years and is still heavily used. In simple terms, principal component analysis (PCA) is a technique to perform dimensionality reduction. You can run the notebook directly in your Browser using Binder. The blog post below contains the same content as the original notebook. ![]() This time, I took a detailed look at principal component analysis (PCA). ![]() ![]() After a longer break I continued working on my machine learning basics repository which implements fundamental machine learning algorithms in plain Python.
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