What: PCA reduces data dimensionality while preserving variance. Transforms correlated variables into uncorrelated principal components.
How: Finds directions of maximum variance in high-dimensional data and projects it onto lower-dimensional space.
Uses: Data visualization ā¢ Noise reduction ā¢ Feature extraction ā¢ Speed up algorithms ā¢ Remove collinearity