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Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru

Author

Listed:
  • Jessie Bravo

    (Professional School of Computer Engineering and Informatics, Pedro Ruiz Gallo National University, Lambayeque 14013, Peru)

  • Roger Alarcón

    (Professional School of Computer Engineering and Informatics, Pedro Ruiz Gallo National University, Lambayeque 14013, Peru)

  • Carlos Valdivia

    (Professional School of Computer Engineering and Informatics, Pedro Ruiz Gallo National University, Lambayeque 14013, Peru)

  • Oscar Serquén

    (Professional School of Computer Engineering and Informatics, Pedro Ruiz Gallo National University, Lambayeque 14013, Peru)

Abstract

Due to the COVID-19 pandemic, the tourism sector has been one of the most affected sectors and requires management entities to develop urgent measures to reactivate and achieve digital transformation using emerging disruptive technologies. The objective of this research is to apply machine learning techniques to predict visitors to tourist attractions on the Moche Route in northern Peru, for which a methodology based on four main stages was applied: (1) data collection, (2) model analysis, (3) model development, and (4) model evaluation. Public data from official sources and internet data (TripAdvisor and Google Trends) during the period from January 2011 to May 2022 are used. Four algorithms are evaluated: linear regression, KNN regression, decision tree, and random forest. In conclusion, for both the prediction of national and foreign tourists, the best algorithm is linear regression, and the results allow for taking the necessary actions to achieve the digital transformation to promote the Moche Route and, thus, reactivate tourism and the economy in the north of Peru.

Suggested Citation

  • Jessie Bravo & Roger Alarcón & Carlos Valdivia & Oscar Serquén, 2023. "Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru," Sustainability, MDPI, vol. 15(11), pages 1-25, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8967-:d:1162144
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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