Implementing factor analysis on multiple variables

Just like PCA, factor analysis can be used for dimensionality reduction. It can be used to condense multiple variables into a smaller set of variables called factors that are easier to analyze and understand. A factor is a latent or hidden variable that describes the relationship of observed variables (i.e., variables captured in our dataset). The key concept is that multiple variables in our dataset have similar responses because they are associated with a specific theme or hidden variable that is not directly measured. For example, responses to variables such as the taste of food, food temperature, and freshness of food are likely to be similar because they have a common theme (factor), which is food quality. Factor analysis is quite popular in the analysis of survey data.

In this recipe, we will explore how to apply factor analysis to a dataset using the factor_analyzer library.

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