Data Analysis using ML on Geolocational Data

Abstract

Machine learning allows us to feed computer algorithms with large amounts of data and make computers analyze and make data-driven decisions and recommendations based solely on input data. This project will utilize ML to analyze geolocational data and user preferences to make smart recommendations to the user . In the fast-paced and busy environment that the average person lives in, it often happens that one is too tired to prepare a home-cooked meal. And of course, even if you get home cooked meals every day, it is not uncommon for you to want to have a good meal every now and then for social / recreational purposes. Now, imagine a scenario where someone has just moved to a new location. They already have certain preferences, certain tastes. It will save a lot of trouble for the student and food suppliers if the student lives near his favorite outlet. The convenience of the means better sales and time savings for customers. This project involves the utilization of K-Means Clustering to seek out the simplest accommodation for students in Bangalore (or the other city of your choice) by classifying accommodation for incoming students on the idea of their preferences on amenities, budget and proximity to the location.