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The Principal Component Analysis is a popular learning technique for decreasing the dimensionality of data. It increases interpretability, and at the same time, it minimizes information loss. It has the most essential quality in a dataset that makes the data easy for plotting in 2D and 3D. The principal component helps in finding a sequence of linear combinations of variables.
In the above figure, we have several points plotted on a 2-D plane. There are two principal components.
PC1 is the primary principal component that explains the maximum variance in the data.
PC2 is orthogonal to PC1.
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