Predicting Specialty Coffee Auction Prices Using Machine Learning

This paper aims to contribute to the coffee pricing literature pertaining to the Cup of Excellence (CoE) competitions by revising the feature set used and extending the modelling approach using machine learning. The specific dataset used is merged from data provided by the Alliance for Coffee Excellence and information collected through scraping public information from the Cup of Excellence website. The paper compares popular supervised learning algorithms exploring multiple interpretations of tasting notes to attain an efficient predictive model of prices. The algorithms compared include OLS, regularised linear algorithms, the decision tree, as well as, bagging and gradient-boosting ensemble methods. The best-performing models are further optimised using hyperparameter tuning and the most efficient one is selected. Based on a gradient-boosting regression, the final model is analysed to find the key relationships driving model predictions. Permutation feature importance and accumulated local effects analyses are used to provide insights into the non-linearities present in the data generating process.