Principal Component Analysis (PCA) is a powerful statistical technique used for dimensionality reduction in data analysis. It transforms a large set of variables into a smaller one while retaining most of the original data’s variability. By identifying the principal components—linear combinations of the original variables—PCA helps to uncover patterns and relationships within the data. This unsupervised method is particularly useful in fields like machine learning and data visualization, as it simplifies complex datasets, making them easier to interpret and analyze. PCA is typically applied to standardized data to ensure accurate results.