Practical Data Science using Python - Principal Component Analysis - Computations 2

The video tutorial explains the process of Principal Component Analysis (PCA) and its application in data analysis. It covers the eigen decomposition process, the role of eigenvectors and eigenvalues, and how they form principal components. The tutorial discusses diagonalization, covariance reduction, and the use of scree plots to determine the explained variance. It concludes with practical steps for applying PCA, including data standardization and recasting using eigenvectors.

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