In many modern applications of machine learning, it is not out of ordinary to be dealing with data consisting of a large number of features. This high dimensionality of the feature space not only presents computational problems in the training of predictive models but also makes the learned model harder to interpret by human users. In this post we discuss a classical and traditionally important technique for reducing the feature dimension of a given dataset, called the Principal Component Analysis.