Principal component analysis

Principal Component Analysis with R: As a student of data analysis, we understand that Principal Component Analysis (PCA) is a powerful tool that helps reduce the dimensionality of large datasets while retaining the most relevant information.

Principal Component Analysis Explained Visually

This formula-free summary provides a short overview about how PCA (principal component analysis) works for dimension reduction, that is, to select k features (also called variables) among a larger set of n features, with k much smaller than n. This smaller set of k features built with PCA is the best subset of k features,… Read More »Introduction to Principal Component Analysis

Principal Component Analysis (PCA) is an indispensable tool for visualization and dimensionality reduction for data science but is often...

How to do Principal Components Analysis (PCA) in R using prcomp(), and how to create beautiful looking biplots using R base functionality, plus how to add confidence ellipses to your plots

Principal Component Analysis

Short form: Win-Vector LLC’s Dr. Nina Zumel has a three part series on Principal Components Regression that we think is well worth your time. Part 1: the proper preparation of data (including…

PCA is a buzz word which always pops up at many stages of Data Analysis for various uses. We explore the intuition behind PCA.

Chapter Outline 1) Overview 2) Basic Concept 3) Factor Analysis Model 4) Statistics Associated with Factor Analysis

Principal Component Analysis (PCA) is the gold standard in dimensionality reduction with uses in business. In 5 minutes, I'll teach you what took me 5 weeks…