Predicting Restaurant Revenue using Machine Learning
DOI:
https://doi.org/10.37134/jcit.vol13.2.2.2023Keywords:
Supervised machine learning, Restaurant revenue prediction, TFI branchAbstract
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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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. https://doi.org/10.1007/S42979-020-00249-1
Ariffin, F. F. (2020). Zara tak putus asa, buka restoran baharu - Berita Harian. https://www.bharian.com.my/hiburan/selebriti/2020/06/704960/zara-tak-putus-asa-buka-restoran-baharu
Bera, S. (2021). An application of operational analytics: For predicting sales revenue of restaurant. In Studies in Computational Intelligence, 907, 209–235. https://doi.org/10.1007/978-3-030-50641-4_13
Chang, X. & Li, J. (2019). Business performance prediction in location-based social commerce. Expert Systems with Applications, 126, 112–123. https://doi.org/10.1016/j.eswa.2019.01.086
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. https://doi.org/10.1016/j.eswa.2010.04.072
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. https://doi.org/10.1007/978-3-540-30116-5_58
Department of Statistic Malaysia. (2020). https://www.dosm.gov.my/v1/index.php?r=column/coneandmenu_id=V3R2ZnB6dE0xU1NDRXNKSHUvdmhkQT09
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. https://doi.org/10.1300/J073v16n02_07
Kolkova, A. (2020). The application of forecasting sales of services to increase business competitiveness. Journal of Competitiveness, 12(2), 90–105. https://doi.org/10.7441/joc.2020.02.06
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. https://doi.org/10.1109/TVCG.2007.70594
McDonald’s: number of restaurants worldwide. Statista. (2021). https://www.statista.com/statistics/219454/mcdonalds-restaurants-worldwide/
Mohamed, A. (2017). Comparative study of four supervised machine learning techniques for classification. Academia.Edu, 7(2). https://www.academia.edu/download/54482697/2.pdf
National Restaurant Association. (2019). Restaurant industry added nearly 14K locations in 2018. NRA. (n.d.). Retrieved from https://restaurant.org/education-and-resources/resource-library/restaurant-industry-added-nearly-14k-locations-in-2018/
Othman, K. (2019). Hanya bertahan 3 bulan, Ning Baizura tutup restoran – Hiburan. mStar. https://www.mstar.com.my/spotlight/hiburan/2019/03/30/ning-tutup-kedai
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. https://doi.org/10.1016/j.ijforecast.2021.06.001
Restaurant Revenue Prediction. Kaggle. (2015). https://www.kaggle.com/c/restaurant-revenue-prediction
Reynolds, D., Rahman, I., & Balinbin, W. (2013). Econometric modeling of the U.S. restaurant industry. International Journal of Hospitality Management, 34(1), 317–323. https://doi.org/10.1016/J.IJHM.2013.04.003
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. https://doi.org/10.1016/j.procir.2019.02.042
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