CLUSTERING ANALYSIS FOR RESIDENTIAL AREAS BASED ON NEIGHBORHOOD AMENITIES

The use of urban land in cities can be improved and the poor execution of Urban planning is related to the problem of housing. The problem of housing has become acute because of the tremendous increase of urban population and unplanned growth of the cities. Mumbai has a population of 20,411,000 thus it is the target of our analysis project. Affordable housing in Mumbai has become an unfathomable challenge, it one of the most complex probes in this city. About 42% of Mumbais housing comprises slums. With this in mind, our aim is to help the decision of buying houses, by recommending localities with basic amenities. We hope to make the process of scrutinizing residential buildings more streamlined. We also hope to underscore areas with housing potential in this study. We use K-Means Clustering to cluster the different neighborhoods of Mumbai, based on the availability of 31 amenities in the neighborhood. We have used Data from Wikipedia to get the list of neighborhoods in Mumbai, and we use Foursquare API to get a list of amenities in each area of the neighborhood. We then evaluate the model using silhouette score and plot a graph using folium to show the different clusters on the map of Mumbai.