Starting a business is never easy, especially in highly competitive cities like Tokyo, Cairo, New York, and many others. This is particularly true for those interested in opening a restaurant, as finding the right location where people will go is challenging due to the high number of existing restaurants in the area. Additionally, areas without restaurants often lack them for a good reason, such as being too dangerous or too expensive.
To solve this problem, we are proposing the Restaurant Popularity Predictor, a tool that leverages Foursquare data to find the best location for a new restaurant. By searching for existing restaurants and collecting their ratings, delivery ids, and locations, we can analyze their data and apply machine learning models to determine the optimal location for a new restaurant that will attract customers.
We will exclusively use Foursquare data for this project, leveraging their restaurant database to gather information about existing restaurants. With this data, we will be able to identify patterns and trends that can help predict the popularity of new restaurants in different areas. By analyzing the data, we hope to identify key factors that contribute to a restaurant's success and use this information to predict the popularity of new restaurants in different locations.
This project is just the starting point, and there is much more that can be done with this data. For example, we could expand our analysis to include data from other sources, such as Yelp or Google Maps. Additionally, we could incorporate demographic data to better understand which types of restaurants are most popular in different areas. By continuing to explore this data, we can gain valuable insights into the restaurant industry and help entrepreneurs succeed in highly competitive markets.