Principal Component Analysis (PCA) is a widely used method for dimensionality reduction in data science and machine learning. It transforms interdependent variables into fewer independent dimensions, making computation more efficient and revealing the dominant structure in complex datasets. PCA provides both mathematical clarity and practical value, helping to work effectively with high-dimensional data.

Principal Component Analysis (PCA) is a widely used method for dimensionality reduction in data science and machine learning. It transforms interdependent variables into fewer independent dimensions, making computation more efficient and revealing the dominant structure in complex datasets. PCA provides both mathematical clarity and practical value, helping to work effectively with high-dimensional data.