To parse this data set, we used the Pandas library from Python on Jupyter Notebooks, and obtained the necessary data to populate our tables. We were able to settle on the following categories to integrate into Tunify: Artists, Genres, and Songs. To populate the database that would be necessary to build Tunify, we found a Spotify dataset from Kaggle with data on various categories involving Spotify’s data. We realized that people universally bond over music, and that we could base an application around that which addresses the current problem and brings about a positive outcome. While social media apps were useful in keeping touch and sharing information with our peers, it was difficult to meet new people this can be especially hard on university students trying to make friends. In the grand scheme of things, Tunify was developed to address the lack of social interaction amidst the current global pandemic. During the process, we learned a variety of tools and technologies while addressing various challenges in making our application the best it can be. In developing Tunify, we gained an understanding of full stack web development by developing a database schema consisting of various tables which we were able to interact with via advanced queries and triggers, a backend REST API using ExpressJS to handle HTTP requests communicating with the database, and a frontend web application designed and programmed with React. They can then view other users with mutual interests, like their profiles, and initiate friendships. Users create an account and proceed to like various artists, genres, and songs. Tunify is a web service through which users can meet those with similar taste in music. UIUC CS 411 Database Systems Final Course Project
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