Principal Component Regression (PCR) - Clearly Explained and Implemented

Understanding the concepts and Python implementation of the regression analysis technique based on principal component analysis (PCA)

Link to article: https://towardsdatascience.com/principal-component-regression-clearly-explained-and-implemented-608471530a2f

Overview

• Principal component analysis (PCA) is a well-known dimensionality reduction technique, but did you know that we can also apply the concepts behind PCA in regression analysis?
• This project provides a clear explanation of principal component regression (PCR), including its theoretical concept, benefits, caveats, and Python implementation.

Contents

• /data: Datasets for the PCR analysis (diamond quality and wine quality data)
• /notebooks: Notebooks demonstrating the implementation of PCR
• /images: Images/screenshots involved in project

Results - Wine Quality Dataset

References

• https://rpubs.com/esobolewska/pcr-step-by-step
• https://blogs.sas.com/content/iml/2017/10/25/principal-component-regression-drawbacks.html
• https://online.stat.psu.edu/stat508/lesson/7/7.1
• https://scikit-learn.org/stable/auto_examples/cross_decomposition/plot_pcr_vs_pls.html