PCA and SVD explained with numpy

How exactly are principal component analysis and singular value decomposition related and how to implement using numpy.

Principal component analysis (PCA) and singular value decomposition (SVD) are commonly used dimensionality reduction approaches in exploratory data analysis (EDA) and Machine Learning. They are both classical linear dimensionality reduction methods that attempt to find linear combinations…