PCA

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May 1, 2024 Updated May 11, 2025 18 minute read

Principal Component Analysis, or PCA, is a statistical technique widely used for simplifying complex datasets. At its core, PCA helps to reduce the number of variables (dimensions) in a dataset while trying to preserve as much of the important information or variance as possible. Imagine you have a dataset with many features; PCA transforms this data into a new, smaller set of features called principal components. These principal components are new variables constructed as linear combinations of the initial variables. They are designed to be uncorrelated and are ordered so that the first few retain most of the variation present in all of the original variables.

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PCA

Path to PCA

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