Abstract
Establishment of a new restaurant requires a paramount investment in it. Thus, a thorough analysis of different factors is important to the determination of the probable rate of success of the restaurant. Among the different factors, location plays a vital role in the determination of the success of the restaurant unit. The demographics of the location, existing cluster of restaurants established in the location, and the growth rate of the location need to be studied prior to selection of the optimal location. However, it is a cumbersome job to find the correct location, by analyzing each of these different aspects of the location. This paper studies the different aspects of a location which makes it a determining factor in the prediction of success of a restaurant. The proposed work aims at the creation of a web application that determines the locations suitable for the establishment of a new restaurant by using techniques of machine learning and data mining.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Raul, N., Shah, Y., Devganiya, M.: Restaurant revenue prediction using machine learning. Int. J. Eng. Sci. 6(4), 91–94 (2016)
Yang, Y., Roehl, W.S., Huang, J.-H.: Understanding and projecting the restaurantscape: the influence of neighborhood socio-demographic characteristics on restaurant location. Int. J. Hosp. Manag. 67, 33–45 (2017)
Mahalingam, S., Jain, B., Sahay, M.: Role of physical environment (Dinescape factors) influencing customers’ revisiting intention to restaurants. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2016)
Tarver, E.: How to Choose the Best Restaurant Location for Your Business. Online document
Unknown Author.: Restaurant market analysis—a guide. http://www.menutek.com/assets/restaurant-market-analysis.pdf
Karen, E., Spaeder.: Online documentation on How to Find the Best Location. https://www.entrepreneur.com/article/73784
Lasek, A., Cercone, N., Saunders, J.: Restaurant sales and customer demand forecasting: literature survey and categorization of methods. In: Leon-Garcia, A., et al. (Eds.) Smart City 360°. SmartCity 360 2016, SmartCity 360 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 166. Springer, Cham (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Das Baksi, B., Rao, V., Anitha, C. (2019). A Survey on Local Market Analysis for a Successful Restaurant Yield. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_22
Download citation
DOI: https://doi.org/10.1007/978-981-13-1498-8_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1497-1
Online ISBN: 978-981-13-1498-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)