Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...

But conceptually, applying PCA to non-numeric data is questionable. The demo program applies z-score normalization to the source data. Next, four eigenvalues and four eigenvectors are computed from ...