Dimensionality Reduction using Factor Analysis: Identifying latent variables that explain the pattern of correlations within a set of observed variables
In many analytics problems, you collect dozens of variables that overlap heavily, survey items that measure similar attitudes, product metrics that move together, or operational KPIs that are tightly correlated. High dimensionality makes models harder to interpret and can amplify noise. Factor Analysis is a practical way to reduce dimensionality by uncovering latent variables (factors)…