principal-component-analysis

Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of data while preserving as much variance as possible. It achieves this by identifying the principal components that capture the highest variance in the dataset. A common misunderstanding is that PCA can be used for all types of data; however, it assumes linear relationships among variables.