AI, Dimensionality Reduction, Machine Learning, Python, Data Science.
2. Collinear Features: When features are highly correlated, PCA can be effective in capturing the shared information and representing it with fewer components. 3. Visualization: PCA is beneficial when visualizing high-dimensional data is challenging. It projects data into a lower-dimensional space