Principal component analysis

Principal Component Analysis (PCA) is a statistical technique used to simplify complex data by reducing its dimensions. Imagine you have a large dataset with many variables, like various features of cars. PCA identifies the most important factors that capture the most variation in the data, transforming the original features into a smaller set of new ones called "principal components." This makes it easier to visualize and analyze the data while retaining its essential structure, helping to uncover patterns and insights without losing significant information.