Thesis: Demand Estimation with Machine Learning
This paper describes a comparison of classical and machine learning methods for the prediction of demand of breakfast cereals. A literature review on predicting demand with discrete choice models and machine learning methods is provided. A utility based linear demand model serves as theoretical basis. The applied methods are explained, then data is simulated to allow for the assignment of coefficients and variation of features. Data pre-processing is done before fitting the models. Results show that machine learning methods outperform classical methods, when working with large number of features.