Cohort 19 Capstone Project for the Graduate Certificate of Data Science at Georgetown University School of Continuing Studies.
Project Detail
Project Topic Create a 20 track generative playlist that collaborates with a Spotify user’s liked tracks to create a lyric driven short story playlist (SSP) where songs glide to each other through a structured narrative.
Members Adam Goldstein (Project Coordinator) - amg413@georgetown.edu Patricia Merino - pm1144@georgetown.edu Navneet Sandhu - nks45@georgetown.edu Nicholas Merkling (Statistician) - nm976@georgetown.edu
General Domain Currently Spotify offers a plethora of playlists ranging from types of moods, and genre, however only one commonly followed playlist declares and puts forward an attempt at a genre-less playlist. This playlist is called Pollen. Pollen is “a playlist based around a radical premise: It was not organized by genre.” This playlist has 410,000 followers and is made up of 120 tracks. A testimony to Pollen’s listeners from Hope Tala, a producer of an artist with 450,000 monthly listeners, exclaims “We’ve been on other playlists that have higher follower numbers, but the engagement on Pollen is mad….This is not like a mid-day cafe background playlist; this is a, I-need-to-listen-to-this-today” (1).
Data Sources Music Database - Train model to find features of “importance” to learn in order to generate SSPs. Spotify API - Use liked tracks to make SSPs based on trained model from Music Database. Kaggle Data File - Train model on lyrics