What is Principal Component Analysis (PCA)?

PCA is a dimensionality reduction technique that transforms data into a new coordinate system where the axes (principal components) are ordered by the amount of variance they explain. The first principal component explains the most variance, the second explains the most remaining variance, and so on. PCA reduces noise, removes correlated features, and speeds up training. It is achieved via eigendecomposition of the covariance matrix. A key decision is how many components to retain — typically chosen to preserve 95% of variance.