Principal Component Analysis (PCA) : Theory

Principal Component Analysis (PCA) is one of the feature extraction methods to identify patterns in data, and expressing the data in such a way as to highlight their similarities and differences. One of the main advantage of PCA is that once these patterns are found in the data, the data can be compressed (i.e. the number of dimensions can be reduced) without much loss of information. This method also solves the problem of correlation among the variables.

If you want to know the math behind this popular algorithm then here you go!!!

Just follow the steps :

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