Principal Components

Principal components are new, simplified variables created from a set of original data with many features. They are designed to capture the most important information by identifying the directions where the data varies the most. Think of it like rotating and compressing a complex dataset into fewer dimensions without losing significant details. This helps in visualizing, analyzing, and reducing large data sets efficiently, making patterns more apparent while preserving as much of the original information as possible.