Practical Guide To Principal Component Methods ... 👑 💫

: It is structured with short, self-contained chapters and "R lab" sections that walk through real-world applications and tested code examples. Core Methods Covered

: It simplifies complex statistical concepts into digestible pieces, focusing on intuitive explanations rather than advanced theory. Practical Guide To Principal Component Methods ...

: Hierarchical Clustering on Principal Components (HCPC), which combines dimensionality reduction with clustering techniques. Who Should Read It : It is structured with short, self-contained chapters

: Simple Correspondence Analysis (CA) for two variables and Multiple Correspondence Analysis (MCA) for more than two. Who Should Read It : Simple Correspondence Analysis

The book categorizes methods based on the types of data you are analyzing:

: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA) for datasets with both continuous and categorical variables.

: The book heavily utilizes the author's own factoextra R package , which creates elegant, ggplot2 -based graphs to help interpret results.