Applies PCA to the scikit-learn breast cancer dataset to reduce feature dimensionality and visualize principal component structure.
load_breast_cancer()
, scales all features with StandardScaler
, then fits PCA
to capture principal components. It examines explained-variance ratios, projects the data onto the first two components for scatter‐plot visualization, and discusses how much variance is retained by successive components.