In today’s fast-paced world, where time is a precious commodity, the ability to order a wide array of cuisines from the comfort of your home or office impacts your quality of life. With an increasing number of food delivery services, with just a few taps on the smartphone or clicks on the computer, we can enjoy the food we want. The importance of this convenience cannot be overstated, as it allows people to save time and effort that would otherwise be spent on cooking, grocery shopping, or dining out. As the food delivery system grows and develops, its economic framework is also in flux. The online food delivery industry is getting highly competitive. It includes several major players, such as Uber Eats and DoorDash in the US, Eat Takeway.com in Europe, and Zomato in several countries, but it broadly operates in India. India is a rapidly growing market among these online food markets with the most enormous population. As reported by IMARC marketing research, "The India online food delivery market size reached US$ 36.3 billion in 2023 and expects the market to reach US$ 257.7 billion by 2032, exhibiting a growth rate (CAGR) of 24.32% during 2024-2032". Making the right data-driven investment decision is essential in this rapidly growing food market. To enable this in this project, we delve into finding answers for how demographic features impact the restaurant business, how the location impacts the restaurants' cuisines, whether international restaurant chains are preferred over local cuisine, and whether there is any correlation between cuisine and restaurant rating. Answering these questions allows new investors to understand the local food market and make data-driven decisions. To answer these questions, we use Zomato's restaurant dataset for Bengaluru. From our analysis, we traverse a diverse array of facets, encompassing the identification of prominent restaurant chains, the categorization of restaurant types, the assessment of online ordering and reservation dynamics, and the creation of geographical distribution maps. Furthermore, to assist this decision-making, we have created a dashboard with all the key indicators and visualizations that summarize the restaurant industry in an area.
Date of Degree
Master of Science (MS)
Program of Study
Computer Information Science
Science, Engineering and Technology
Shah, R. (2023). Making data-driven decisions for investing in restaurant business: A case study based on Zomato dataset. [Master’s alternative plan paper, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/1380/