Student residential distance calculation using Haversine formulation and visualization through GoogleMap for admission analysis

Data mining is defined as extracting and analyzing information from various heterogeneous data sources to generate the user interested patterns. In this paper, data mining is used for such a purpose where in which it helps in increasing the marketing of a respective educational organization. Students will come from different localities to join a prospective college. In the proposed system, instead of places, the distance from the address of the student residence destination is analyzed. It provides a more accurate idea about the upcoming year marketing. Distance can be calculated by using the Haversines formula and the clustering algorithm k-means can be used to cluster the locations to get more accurate results. Google maps API is used to find out the latitude and longitude of each student residential address and visualized, which gives the minimum, maximum and average distance. The pictorial representation helps the organizations to concentrate more on specific areas where the better advertisement can be given to improving the admission rate.