The Benefits of Automated Machine Learning in Hospitality: A Step-By-Step Guide and AutoML Tool

Mauro Castelli, Diego Costa Pinto, Saleh Shuqair, Davide Montali, Leonardo Vanneschi


The manuscript presents a tool to estimate and predict data accuracy in hospitality by means of automated machine learning (AutoML). It uses a tree-based pipeline optimization tool (TPOT) as a methodological framework. The TPOT is an AutoML framework based on genetic programming, and it is particularly useful to generate classification models, for regression analysis, and to determine the most accurate algorithms and hyperparameters in hospitality. To demonstrate the presented tool’s real usefulness, we show that the TPOT findings provide further improvement, using a real-world dataset to convert key hospitality variables (customer satisfaction, loyalty) to revenue, with up to 93% prediction accuracy on unseen data.


Doi: 10.28991/ESJ-2022-06-06-02

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Artificial Intelligence; Automated Machine Learning; Behavioral Research; Hospitality.


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DOI: 10.28991/ESJ-2022-06-06-02


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