Enhancing Spotify Playlists using their Audio Features with Classical and Deep Learning Methods

In today's music streaming landscape, platforms like Spotify are becoming increasingly relevant. Part of what makes Spotify such a dominant platform in the space is the recommendation algorithm it uses. Hundreds of millions of users on the platform use the recommendation algorithm to both expand their tastes and get more of what they love. Using data on over a million songs and their features, as well as thousands of playlists, we were able to develop two distinct algorithms to develop our own recommendation engine. The two models we made utilize two very different approaches; one is a classic KNN clustering algorithm, while the other is a deep learning approach leveraging a neural network. In order to provide a cohesive recommendation system, we would need a lot of data -- data consisting of features from millions of songs -- from Spotify, so that we can train our models on these songs to provide accurate recommendations.