This team went above and beyond to shape and support the architecture of the project, they also helped deployment of the related pipelines. The team also contributed meaningfully to aspects of the deployed project beyond Data Science, smoothed out conflicts, and significantly contributed to explaining the project to non-DS people beyond the team.
The Song Suggester App Takes a song's reference (name, artist) provided by the user, searches for that song's information on the Spotify database to use as starting point to gather a list of related songs. Gather a list of 30 related songs, collecting the top 10 tracks of the provided song artist, One track from 10 related artists based on Spotify users reproductions, and 10 songs based on the genre and artist of the user's song. After the described process all the audio features of each of the 30 tracks are compared with the user's song through an ML algorithm and reordered to provide the user with the top 5 songs that are the most closely related to his favorite one.
Mitch Hollberg DS-32 Github: LinkedIn:
Zachary Quintana DS-32 Github: LinkedIn:
German Parra DS-33 Github: LinkedIn:
Matt Grohnke DS-33 Github: LinkedIn:
John A. Baker Jr. DS-33 Github: LinkedIn:
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