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What is Understanding Principal Component Analysis about?

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated variables into uncorrelated ones while maximizing variance retention. Introduced by Karl Pearson in 1901, PCA is widely used in exploratory data analysis and machine learning for tasks such as data visualization, feature selection, and data compression. By identifying principal components that capture the most variance, PCA simplifies complex datasets, aiding in better interpretation and analysis.

Author

Shobha Kumari Choudhary

Language

EN