Data Analysis using ML on Geolocational Data

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. For example, imagine a case where someone has just shifted to a new place. They already have certain preferences, certain tastes. It will save a lot of trouble for the person and food suppliers if that particular person lives near his favorite outlet. The convenience of the means better sales and time savings for customers. This project involves using K-Means Clustering to find the easiest food location for students in Greater Noida (or any other city of your choosing) by categorizing popular snack destinations for incoming students based on their mood and preferences for amenities, affordability, and closeness to the place!