What is the principal component analysis? Explain the sort of problems you would use PCA for

Data Science, AI and ML
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#1

In its simplest sense, PCA involves project higher dimensional data (eg. 3 dimensions) to a smaller space (eg. 2 dimensions). This results in a lower dimension of data, (2 dimensions instead of 3 dimensions) while keeping all original variables in the model.

PCA is commonly used for compression purposes, to reduce required memory and to speed up the algorithm, as well as for visualization purposes, making it easier to summarize data.