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Predicting Specialty Coffee Auction Prices Using Machine Learning

Author

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  • Aldott, Zoltan

    (University of Warwick)

Abstract

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.

Suggested Citation

  • Aldott, Zoltan, 2021. "Predicting Specialty Coffee Auction Prices Using Machine Learning," Warwick-Monash Economics Student Papers 15, Warwick Monash Economics Student Papers.
    • Handle: RePEc:wrk:wrkesp:15
    as

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    File URL: https://warwick.ac.uk/fac/soc/economics/research/wmesp/manage/15_-_zoltan_aldott.pdf
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    References listed on IDEAS

    as
    1. Donnet, M. Laura & Weatherspoon, Dave D., 2006. "Effect of Sensory and Reputation Quality Attributes on Specialty Coffee Prices," 2006 Annual meeting, July 23-26, Long Beach, CA 21388, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Adam P. Wilson & Norbert L.W. Wilson, 2014. "The economics of quality in the specialty coffee industry: insights from the Cup of Excellence auction programs," Agricultural Economics, International Association of Agricultural Economists, vol. 45(S1), pages 91-105, November.
    3. Wilson, Norbert, 2014. "When Higher Quality Does Not Translate to Higher Prices: A Case of Quality and Specialty Coffees from the Cup of Excellence Auctions," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170701, Agricultural and Applied Economics Association.
    4. Ramirez, Carlos D., 2010. "Do Tasting Notes Add Value? Evidence from Napa Wines," Journal of Wine Economics, Cambridge University Press, vol. 5(1), pages 143-163, April.
    5. Meike Wollni & Manfred Zeller, 2007. "Do farmers benefit from participating in specialty markets and cooperatives? The case of coffee marketing in Costa Rica1," Agricultural Economics, International Association of Agricultural Economists, vol. 37(2‐3), pages 243-248, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    specialty coffee ; machine learning ; prediction ; Coffee Taster’s Flavor Wheel ; Cup of Excellence JEL Classification: C53 ; C81 ; D44 ; Q11;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

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