İstanbul’daki Restoranlar için Makine Öğrenmesi Algoritmaları Kullanılarak Fiyat Tahmin Modeli

Abstract

Günümüzde veri bilimi kavramıyla birlikte firmalar veriye dayalı yeni ürünler ortaya çıkarmış ve işletmelerin dijitalleşme süreçlerini hızlandırmıştır. Bu çalışmada İstanbul’da bulunan yeme-içme sektöründeki işletmelerin verileri toplanarak makine öğrenmesi algoritmaları ile bir fiyat tahmin modeli önerilmiştir. Bu çalışmada restoran fiyat tahmini için XGBoost, Random Forest, Artificial Neural Network, K-Nearest Neighbor, Multi Linear Regression ve CatBoost gibi farklı makine öğrenmesi modelleme algoritmaları kullanılmıştır. Fiyat tahmini için sınıflandırma algoritmaları test edilmiş ve değerlendirme sonucunda XGBoost algoritmasının 0.023236 RMSE ve 0.0005399 MSE hata oranları ile en iyi algoritma olduğu gözlemlenmiştir. Bu çalışma sayesinde işletme sahipleri işletmelerinde yapacakları yeni geliştirmelerin fiyat ve müşteri geri bildirimi açısından ne derece fayda sağlayacağını anlayabileceklerdir. Girişimcilerin yeni kuracakları bir işletmenin hangi özelliklere sahip olması gerektiğini ve bu özelliklere göre müşterilerine sunacakları ortalama fiyat konusunda bilgi sahibi olmasını sağlayacaktır. Ayrıca restoran açmak isteyen girişimcilerin neye ne kadar maliyet koyması gerektiğini öğrenecek, fiyat performansı sağlayacak, ürünlerini makul fiyata satacağı için daha çok ürün satıp karlılığını arttıracaktır. Doğru fiyatlandırma pazarlamanın dört önemli kavramlarından biridir. Firmanın tutunması, müşteri sadakati oluşturması için doğru fiyatlandırma yapması gerekmektedir.

Keywords

• Veri Bilimi
• Fiyat Stratejisi
• Veri Yönetimi
• Tahmin Modellemesi
• Sınıflandırma Algoritmaları

Price Prediction Model for Restaurants In Istanbul By Using Machine Learning Algorithms

Abstract

Today, companies have created new products based on data and accelerated the digitalization processes of businesses with the concept of data science. In this study, a price prediction model is proposed with machine learning algorithms by collecting the data of businesses in the food and beverage sector in Istanbul. In this study, different machine learning modeling algorithms such as XGBoost, Random Forest, Artificial Neural Network, K-Nearest Neighbor, Multi Linear Regression and CatBoost were used for restaurant price prediction. Classification algorithms were tested for price prediction, and as a result of the evaluation, it was observed that XGBoost algorithm achieve the highest performance with 0.023236 RMSE and 0.0005399 MSE error rates. By this study, business owners will be able to understand how new developments they will make in their businesses will benefit in terms of price and customer feedback. It will enable entrepreneurs to have information about what features a new business should have and the average price they will offer to their customers according to these features. In addition, entrepreneurs who want to open a restaurant will learn how much they should cost, provide price performance, and increase their profitability by selling more products because they will sell their products at affordable prices. Accurate pricing is one of the four important concepts of marketing. The company needs to make the right pricing in order to hold on and create customer loyalty.

Keywords

• Data Science
• Price Strategy. Data Management
• Prediction Model
• Classification Algorithms.

References

1. Alshari, H., Saleh, A. Y., & Odabas, A. (2021). Comparison of Gradient Boosting Decision Tree Algorithms for CPU Performance. Erciyes University Journal of Institue Of Science and Technology, 160-161.
2. Breinman, L. (2001). Random Forest. University of California, 5-32.
3. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
4. Cinaroglu, S. (2017). Comparison of Machine Learning Regression Methods to Predict Health Expenditures. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 189.
5. Cover, T., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE, 21-27.
6. Ding, S., & Chen, L. (2010). Intelligent Optimization Methods for High-Dimensional Data Classification for Support Vector Machines. Intelligent Information Management, 1-12.
7. Eslami, S. P., Ghasemaghaei, M., & Hassanein, K. (2018). Which online reviews do consumers find most helpful? A multi-method investigation. Decision Support Systems, 113, 32-42.
8. Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 1189-1232.

9. Gu, Z. (2002). Analyzing bankruptcy in the restaurant industry: A multiple discriminant model. International Journal of Hospitality Management, 21(1), 25-42.
10. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 211.
11. Kim, S. Y., & Upneja, A. (2014). Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models. Economic Modelling, 36, 354-362.
12. Kervancı, I. S., & Akay, M. (2020). Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods. Sakarya University Journal of Computer and Information Sciences, 273-281.
13. Kukreja, H., N, B., S, S. C., & S, K. (2016). AN INTRODUCTION TO ARTIFICIAL NEURAL NETWORK. Journal of Electrical & Electronics Engineering, School of Engineering & Technology, Jain University, 27-29.
14. Kulkarni, A., Bhandari, D., & Bhoite, S. (2019). Restaurants Rating Prediction using Machine Learning Algorithms. International Journal of Computer Applications Technology and Research.
15. Li, M., Huang, L., Tan, C. H., & Wei, K. K. (2013). Helpfulness of online product reviews as seen by consumers: Source and content features. International Journal of Electronic Commerce, 17(4), 101-136.
16. Litvin, S. W., Goldsmith, R. E., & Pan, B. (2018). A retrospective view of electronic word-of-mouth in hospitality and tourism management. International Journal of Contemporary Hospitality Management.
17. Lunkad, K. (2015). Prediction of Yelp Rating using Yelp Reviews.
18. Meek, S., Wilk, V., & Lambert, C. (2021). A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews. Journal of business research, 125, 354-367.
19. Osman, T., Mahjabeen, M., Psyche, S. S., Urmi, A. I., Ferdous, J. S., & Rahman, R. M. (2016, June). Adaptive food suggestion engine by fuzzy logic. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) (pp. 1-6). IEEE.
20. Schmidt, A., Kabir, M. W. U., & Hoque, M. T. (2022). Machine Learning Based Restaurant Sales Forecasting. Machine Learning and Knowledge Extraction, 4(1), 105-130.
21. Shihab, I. F., Oishi, M. M., Islam, S., Banik, K., & Arif, H. (2018, December). A machine learning approach to suggest ideal geographical location for new restaurant establishment. In 2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 1-5). IEEE.
22. Tsoumakas, G. (2019). A survey of machine learning techniques for food sales prediction. Artificial Intelligence Review, 52(1), 441-447.
23. Vicario- Becerra, R., Alaminos, D., Aranda, E., & Fernández-Gámez, M. A. (2020). Deep recurrent convolutional neural network for bankruptcy prediction: A case of the restaurant industry. Sustainability, 12(12), 5180.
24. Wang, A., Zeng, W., & Zhang, J. (2016). Predicting New Restaurant Success and Rating with Yelp. ser. CS221: Artificial Intelligence: Principles and Techniques, Stanford University.