How do you get to pca?

Answer 1

To get to PCA (Principal Component Analysis), you first standardize your data to ensure each feature contributes equally. Next, you compute the covariance matrix to understand how variables interact. Then, you perform eigenvalue decomposition on the covariance matrix to identify principal components, which are the new axes that capture the most variance in the data. Finally, you project your original data onto these principal components for dimensionality reduction.