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Predicting Restaurant Revenue using Machine Learning

Authors

  • Nadiah Hanun Ismail School of Management, Universiti Sains Malaysia, Penang, Malaysia.
  • Chee-Wooi Hooy School of Management, Universiti Sains Malaysia, Penang, Malaysia.

Keywords:

Supervised machine learning, Restaurant revenue prediction, TFI branch

Abstract

This paper studied the restaurant branch’s revenue to determine the best strategic location with period of study from 1996 until 2014. On the other hand, the paper examined multiple linear regression, decision tree regression, random forest regression and support vector regression to forecasting approach that will likely generate the highest accuracy during validation process in predicting the revenue. Analysis have resulted that support vector regression gives the lowest of error. Some recommendation been proposed for successful plans toward revenue growth which applicable to adopt in the company.

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References

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Published

2023-02-21

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How to Cite

Ismail, N. H., & Hooy, C.-W. (2023). Predicting Restaurant Revenue using Machine Learning. Journal of Contemporary Issues and Thought, 13(2), 9–22. Retrieved from https://ojs.upsi.edu.my/index.php/JCIT/article/view/7677