DATA SCIENCE THROUGH R UNSUPERVISED LEARNING. DIMENSION REDUCTION TECHNIQUES: PRINCIPAL COMPONENTS, FACTOR ANALYSIS AND CORRESPONDENCE ANALYSIS

DATA SCIENCE THROUGH R UNSUPERVISED LEARNING. DIMENSION REDUCTION TECHNIQUES: PRINCIPAL COMPONENTS, FACTOR ANALYSIS AND CORRESPONDENCE ANALYSIS

Data science algorithms use computational methods to extract information directly from data. Machine learning uses two types of techniques: supervised learning, which trains a model with known input and output data so that it can predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. Most of the unsupervised analysis techniques related to dimension reduction are developed throughout this book from a methodological and practical perspective with applications through Python software. The following techniques are explored in depth: Principal Components Analysis, Factor Analysis, Simple Correspondence Analysis, and Multiple Correspondence Analysis.