Why use principle component analysis?

Principal Component Analysis (PCA) is used to reduce the dimensionality of large datasets while preserving as much variance as possible. By transforming correlated variables into a smaller set of uncorrelated variables (principal components), PCA simplifies data analysis, visualization, and modeling. This technique helps to identify patterns and relationships in data, mitigate noise, and improve computational efficiency in machine learning tasks. Overall, PCA enhances interpretability and can aid in feature selection.