Transforming Datasets with PCA

PCA is one of the most widely used techniques for dimensionality reduction in ML and data analysis. It helps simplify datasets by transforming them into a new coordinate system, where the greatest variance in the data is captured by the first few dimensions, called principal components. In this recipe, you will learn how to implement PCA using scikit-learn, interpret the results, and apply PCA to various datasets to effectively reduce feature dimensionality.

Getting ready

To begin, we will load our toy dataset from scikit-learn. Version 1.5 of scikit-learn contains 6 datasets that are commonly used to illustrate various ML steps and features in the library. In this case, we will be using the Wine dataset.

1. Load libraries. We will also load the warnings library to suppress warning messages that could clutter up our Notebook.

   import numpy as np
   import...