Principal Component Analysis (PCA) is the most widely used linear method in dealing with dimension reduction problems. It is useful when data contains many features, and there is redundancy (correlation) within these features. To remove redundant features, PCA maps high dimension data into lower dimensions by reducing features into a smaller number of principal components that account for most of the variance of the original features. In this recipe, we will look at how to perform dimension reduction with the PCA method.
Performing dimension reduction with PCA
Getting ready
In this recipe, we will use the swiss dataset as our target to perform PCA. The swiss dataset includes standardized fertility measures and socio-economic...
