What does PCA stand for in data analysis?

PCA stands for Principal Component Analysis, a fundamental technique in data analysis and statistics. It is primarily used for dimensionality reduction while preserving as much variance as possible in the dataset. The process involves identifying the directions (or principal components) that maximize the variance in the data. By transforming the original variables into a new set of uncorrelated variables that are ordered by the amount of original variance they capture, PCA allows analysts to simplify complex datasets and visualize them more effectively. This is especially useful in scenarios where datasets have many variables, as it helps in reducing noise and computation while retaining essential information.