Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a widely used linear dimensionality reduction technique (of type Feature Extraction) used for reducing the dimensionality of datasets containing many correlated variables while preserving most of the variability in the data. Here’s how PCA works: Each of the “new” variables after PCA are all independent of one another. PCA has…