Neighborhood Segmentation and Clustering Using K-Medoids Algorithm and Location-Based Social Network

For an effective business, a decent location is required where there is less competition for similar kinds of businesses. In the proposed work, venue data is used to recommend good locations for users to open new venues in any neighborhood by selecting the finest locality in the city. Suppose, a person lives in the neighborhood of a city where amenities like hotels, malls, and restaurants are nearby. Due to a job opportunity, the person has to shift to another neighborhood in that city. So, isn't it good that the person can find neighborhoods that are similar to his/her current neighborhood? So, the proposed work also helps to discover similar neighborhoods. Data Science tools, Foursquare, K-medoids (Unsupervised Clustering Algorithm) are applied to segregate the neighborhoods into clusters and to obtain useful outcomes.