![]() ![]() It is also a useful technique if you are working on regression modeling and there is multicollinearity present in your data. Principal component analysis can be used as a dimension reduction technique where we form new variables that are linear combinations of the original variables. Visualizing PCA results in R with ggplot2 and factoextra.The math of PCA: how to calculate the principal components.Visualizing PCA results is very easy in R using the factoextra and gglot2 packages. I will also show how we can find and visualize the results after performing a small PCA on the iris dataset using sklearn in Python, and R. ![]() In this guide to the Principal Component Analysis, I will give a conceptual explanation of PCA, and provide a step-by-step walkthrough to find the eigenvectors used to calculate the principal components and the principal component scores. ![]()
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March 2023
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