A case study on how to predict café profit: A dimension reduction via factor analysis

Abstract

The purpose of this paper is to confirm the improvement of accuracy in predicting the profit of a café by using dimensionality reduction features through Factor Analysis. Profit forecasts for retailers have always been of great interest. We limit the discussion to the prediction of a café profit. We show that dimensional reduction through Factor Analysis is useful for various types of data. After that, we compare the SVM with the linear regression and show that using a good kernel trick of the SVM improves accuracy.