Predicting Restaurant Revenue using Machine Learning


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



Supervised machine learning, Restaurant revenue prediction, TFI branch


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.


Download data is not yet available.


Ahmad, I. S., Bakar, A. A., Yaakub, M. R., & Muhammad, S. H. (2020). A survey on machine learning techniques in movie revenue prediction. SN Computer Science, 1(4), 1–14.

Ariffin, F. F. (2020). Zara tak putus asa, buka restoran baharu - Berita Harian.

Bera, S. (2021). An application of operational analytics: For predicting sales revenue of restaurant. In Studies in Computational Intelligence, 907, 209–235.

Chang, X. & Li, J. (2019). Business performance prediction in location-based social commerce. Expert Systems with Applications, 126, 112–123.

Chen, C.-Y., Lee, W.-I., Kuo, H.-M., Chen, C.-W., & Chen, K.-H. (2010). The study of a forecasting sales model for fresh food. Expert Systems with Applications, 37(12), 7696–7702.

Demšar, J., Zupan, B., Leban, G., & Curk, T. (2004). Orange: From Experimental Machine Learning to Interactive Data Mining. In European conference on principles of data mining and knowledge discovery (pp. 537-539). Springer, Berlin, Heidelberg.

Department of Statistic Malaysia. (2020).

Gogolev, S. & Ozhegov, E. M. (2019). Comparison of machine learning algorithms in restaurant revenue prediction. Communications in Computer and Information Science, 1086CCIS, 27–36.

Hájek, P. & Olej, V. (2010). Municipal revenue prediction by ensembles of neural networks and support vector machines. WSEAS Transactions on Computers, 9(11), 1255–1264.

Hu, C., Chen, M., & McCain, S.-L. C. (2004). Forecasting in short-term planning and management for a casino buffet restaurant. Journal of Travel and Tourism Marketing, 16(2–3), 79–98.

Kolkova, A. (2020). The application of forecasting sales of services to increase business competitiveness. Journal of Competitiveness, 12(2), 90–105.

Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging Artificial Intelligence Applications in Computer Engineering, 160(1), 3-24.

Mackinlay, J., Hanrahan, P., & Stolte, C. (2007). Show me: Automatic presentation for visual analysis. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1137–1144.

McDonald’s: number of restaurants worldwide. Statista. (2021).

Mohamed, A. (2017). Comparative study of four supervised machine learning techniques for classification. Academia.Edu, 7(2).

National Restaurant Association. (2019). Restaurant industry added nearly 14K locations in 2018. NRA. (n.d.). Retrieved from

Othman, K. (2019). Hanya bertahan 3 bulan, Ning Baizura tutup restoran – Hiburan. mStar.

Posch, K., Truden, C., Hungerländer, P., & Pilz, J. (2021). A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants. International Journal of Forecasting.

Restaurant Revenue Prediction. Kaggle. (2015).

Reynolds, D., Rahman, I., & Balinbin, W. (2013). Econometric modeling of the U.S. restaurant industry. International Journal of Hospitality Management, 34(1), 317–323.

Tanizaki, T., Hoshino, T., Shimmura, T., & Takenaka, T. (2019). Demand forecasting in restaurants using machine learning and statistical analysis. Procedia CIRP, 79(2), 679–683.



2023-02-21 — Updated on 2023-04-22


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. (Original work published February 21, 2023)