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.

What is the role of the covariance matrix in PCA?

Explain the eigen decomposition process in the context of PCA.

What are eigenvectors and eigenvalues, and how do they relate to PCA?

How does PCA help in reducing the dimensionality of a dataset?

What is diagonalization in the context of PCA?

Discuss the implications of having strong correlations between features in a dataset.

What is a scree plot and how is it used in PCA?

Describe the significance of the explained variance ratio in PCA.

How do you determine the number of principal components to retain?

What steps are involved in the PCA process from the original dataset to the final output?