PCA (Principal Component Analysis)

Principal Component Analysis (PCA) is a statistical technique used to simplify complex data by identifying the most important patterns. It transforms the original data into a new set of variables called "principal components," which capture the maximum variation in the data. These components are uncorrelated and ranked so that the first few retain most of the important information. PCA helps reduce the number of variables needed, making data easier to analyze and visualize without losing significant insights. It's commonly used in fields like image recognition, finance, and scientific research to find structure and simplify data analysis.