What is the main purpose of Principal Component Analysis (PCA)?
A. Principal Component Analysis is a technique to reduce the dimensionality of the data set. As a result of PCA, you get principal components that are linear combinations of original variables. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. By using only the first few principal components, it is possible to reduce the dimensions of the data, while maintaining the maximum possible variance.
