This project was done as a part of the IBM Data Science Professional Certificate Capstone. We use clustering, based on restaurants and the 10 most popular cuisines, to cluster similar localities together. Another approach we use is that we consider the average cost for two people, for restaurants in a given locality. We then cluster similar localities, based on the average spending for two people at restaurants present in that locality. This gives us a rough idea of how posh or high end a locality may be. A high-end upcoming restaurant can be placed in localities that have other restaurants with similar average costs. We can also use the popularity of cuisines to suggest cuisines to restaurants that can be opened in a particular area. More details can be obtained from the report and presentation.
Dataset credits: Shruti Mehta, Kaggle