Communications for Statistical Applications and Methods

  • 제30권5호
  • /
  • Pages.485-499
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  • 2023
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Comparative analysis of model performance for predicting the customer of cafeteria using unstructured data

  • Seungsik Kim (Department of Statistics, Kyungpook National University) ;
  • Nami Gu (Department of Statistics, Pusan National University) ;
  • Jeongin Moon (Department of Statistics, Yeungnam University) ;
  • Keunwook Kim (Daegu Digital Innovation Agency, Big Data Utilization Center) ;
  • Yeongeun Hwang (Industrial Complex Promotion Department, Korea Industrial Complex Corporation) ;
  • Kyeongjun Lee (Department of Mathematics and Big Data Science, Kumoh National Institute of Technology)
  • 투고 : 2023.03.17
  • 심사 : 2023.06.04
  • 발행 : 2023.09.30
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초록

This study aimed to predict the number of meals served in a group cafeteria using machine learning methodology. Features of the menu were created through the Word2Vec methodology and clustering, and a stacking ensemble model was constructed using Random Forest, Gradient Boosting, and CatBoost as sub-models. Results showed that CatBoost had the best performance with the ensemble model showing an 8% improvement in performance. The study also found that the date variable had the greatest influence on the number of diners in a cafeteria, followed by menu characteristics and other variables. The implications of the study include the potential for machine learning methodology to improve predictive performance and reduce food waste, as well as the removal of subjective elements in menu classification. Limitations of the research include limited data cases and a weak model structure when new menus or foreign words are not included in the learning data. Future studies should aim to address these limitations.

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참고문헌

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