ABSTRACT
People visit many places regularly and they would obviously want o try the best of the food that is available in that place. In addition, a restaurant has many attributes like price range and customer rating, etc. This extra information would assist in making a decision about the location. Bringing together the location of venues in the city with their attributes would surely guide visitors in a city to make better-informed decisions about the places they should visit. We use data from the Foursquare and Zomato API for segmentation of the data and drawing plots on various features like price range, user rating, etc. Then the K-means clustering algorithm is applied for clustering the venues to get information about the clusters of venues and to provide better recommendations.
When looking for a venue in a new location, people are looking for the best places a city has to offer. The person may wish to know a restaurant’s quality or price range. Combining the location of venues in a city with their ratings and prices would undoubtedly help tourists make better decisions about where to go.
