Principal Component Analysis
Principal Component Analysis is a statistical technique used to reduce the dimensionality of a dataset while retaining as much variance as possible. It works by identifying the underlying patterns in the data and representing them as a set of new orthogonal variables called principal components. These components are ordered by the amount of variance they explain, with the first component explaining the most variance in the data. By projecting the data onto a subset of these principal components, PCA can simplify complex datasets and aid in visualization and interpretation.