Canonical Correlation Analysis (CCA) identifies relationships between two multivariate datasets by finding linear combinations that maximize their correlation. Used in fields like genomics, finance, and marketing, it uncovers hidden connections between variables such as gene expression and clinical outcomes. Researchers, data scientists, and analysts benefit from CCA to simplify complex, high-dimensional data.
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