Location-Based Ideal Site Selection using Clustering

Selecting a business site is a significant decision in managing a commercial organization. It becomes more complex in countries like India, where population density is high, and it is tough to distribute services fairly. With this difficulty, investing in a location without a thorough survey is challenging. This study aims to provide an information map that can help choose the optimal neighborhood for a particular business. The research aims to use data science powers to provide an unsupervised machine-learning approach to offer a recommendation-based solution that suggests the best location for a given use case. The objective of this paper is the in-depth analysis for site-finding that includes clustering regions based on various factors such as geographical locations (like schools, bus and railway stations, and hospitals), sanitation, distance, footfall, and other entities. The idea is to obtain the user's use case query and then create hotspots on the map that show the most suitable locations where the specific use case will benefit the stakeholders the most. We need to identify neighborhoods where there may be an unsatisfied need for Indian restaurants. Additionally, we require places that are uncrowded and have little competition. If the first two requirements are satisfied, we would also like a location close to a prominent city neighborhood. The advantages of each area will then be clearly expressed so that stakeholders can choose the best possible final location.