Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of large datasets by transforming them into a new set of variables called principal components. These principal components are uncorrelated and arranged in such a way that the first few retain most of the variation present in the original dataset. PCA is widely used in various fields, including Catalysis, for simplifying complex data, enhancing interpretability, and improving computational efficiency.
