Data Analysis for Students Based on Geolocation Approach

It is quite important for students, particularly those who are new to strange places, to find appropriate housing. Some viable solutions to this difficulty can be found in advanced analytical approaches such as geolocational analysis and clustering algorithms. The optimal student housing options in any city can be found by thoroughly exploring geolocational data, as demonstrated in this study. In order to organise housing possibilities according to criteria such as budget, proximity to amenities, and availability, we employ the popular K-Means Clustering technique. Our objective is to provide new students with tailored recommendations by grouping homes into clusters; this will simplify their search and increase their level of satisfaction. This project seeks to demonstrate how geolocational analysis and clustering can improve student housing selection, which in turn improves students' health and academic performance.