Publication Date: 2023/09/09
Abstract: Students in Various colleges travel from far away places for a better education. Here they often face problem while looking for an affordable and safe accommodation. Additionally, users often have to check out the places as there is very little information in amenities , location and user feedback. Not only this but also often, people are too tired to prepare home-cooked meals due to the fast-paced and busy environment where they live. Furthermore, even if you eat only homemade food every day, you are still likely to want to go out to eat for social or recreational purposes from time to time. Despite this, it's common knowledge that food is an important aspect of anyone's lifestyle, no matter where they live. Assume, for example, someone has just moved to a new home. Their preferences and tastes are already established. If a student lives nearby her favourite outlet, this will save her a lot of trouble and help her save money. Students nowadays also have to take care of their health and fitness. Hence, recommendation for affordable gyms has also become a requirement. Based on incoming students' preferences for facilities, budget, and proximity to the location, this project uses K-Means Clustering to find the most suitable accommodation, restaurant as well as gyms for them in Akurdi (Pune).
Keywords: Geolocation data analysis, k- means clustering, python, recommendation.
DOI: https://doi.org/10.5281/zenodo.8330773
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23MAY1418.pdf
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