Principal Component Analysis

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PCA

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is a widely used technique for dimensionality reduction in data science. PCA's relationship with linear algebra is central to its functioning and effectiveness. Linear algebra provides the mathematical framework for transforming, extracting meaningful information, and reducing the dimensionality of the data while preserving its essential characteristics. Understanding the underlying linear algebra concepts is essential for grasping the theory and implementation of PCA effectively. In this question, you will use PCA to reduce the dimensionality of a dataset of your choice. In this text box, explain what the results mean. What did we do in this problem and why did we do it$?$