Principal Component Analysis (PCA) on Coh-Metrix 108 Features

Principal Component Analysis (PCA) is a technique used for dimensionality reduction and feature extraction. It’s commonly applied to high-dimensional datasets to capture the most important information while reducing the number of features. Here’s a general outline of how you can perform PCA on a high-dimensional feature matrix using Python and the popular machine learning library, scikit-learn: Code