Introduction. Principal Component Analysis (PCA) is a…

Principal Component Analysis (PCA) is a popular data analysis technique used to reduce the dimensionality of a dataset. It is widely used in many fields, including machine learning, computer vision, and bioinformatics. PCA is a statistical method that transforms a dataset into a new coordinate system, where the variables are uncorrelated and ranked in order of their contribution to the variance in the data. This new coordinate system is called the principal components. The first principal component accounts for the maximum amount of variance in the data, followed by the second, and so on.