MULTIVARIATE DATA ANALYSIS TECHNIQUES USING PYTHON. DIMENSION REDUCTION, CLASSIFICATION AND SEGMENTATION

When faced with the reality of a study, the researcher usually has many variables measured or observed in a collection of individuals, intends to study them together, and turns to Multivariate Data Analysis. They are faced with a variety of techniques and must select the most appropriate for their data, but, above all, for their scientific objective. Multivariate Analysis uses two types of techniques: supervised learning, which trains a model with known input and output data so that it can predict future results, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. Most of the unsupervised analysis techniques are developed throughout this book from a methodological and practical perspective with applications through the Python software. The following techniques are explored in depth: Dimension Reduction, Principal Components Analysis, Factor Analysis, Simple Correspondence Analysis, Multiple Correspondence Analysis, Multidimensional Scaling, Cluster Analysis, and Discriminant Analysis.