Principal component analysis is a statistical technique of factor analysis and dimensionality reduction that transforms a set of possibly correlated initial features into a smaller set of linearly uncorrelated features called principal components. In this way, PCA preserves as much variance in the dataset as possible, while minimizing the number of features.