What is PCA (Principal Component Analysis) and when is it used?

PCA is a dimensionality reduction technique used to reduce the number of features in a dataset while retaining as much variance as possible. It projects the data onto a set of orthogonal axes (principal components) that maximize variance. PCA is useful for visualizing high-dimensional data and improving model performance by reducing noise.