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Building A Recommender Using Spotify's Web API: "Spotipy"

This thesis project is conducted independently under UC Berkeley's Data Science Honors Program and is advised by Professor Zachary A. Pardos (PhD).

Goal

The goal of this project is to build a content-based recommender for music listeners, using data from Spotify’s Web API.

Data Set

For this project, we rely on Spotipy as the main source of data extraction. Spotipy is a “lightweight python library” for Spotify’s Web API that gives developers full access to all of Spotify’s music data. For the purposes of this project, the dataset consists of the following features: danceability, energy, key, loudness, mode, speechiness, acousticness, instrumentalness, liveness, valence (positiveness), tempo, song_type, id, uri, track-href, analysis_url, duration_ms, time_signature, genre, album, popularity (popularity score), artist_name, and track_name. They contain raw data on the sound and musicality of each soundtrack, reducing any kind of bias and censoring of data. We note that popularity scores may contain bias based on the user who gave the rating.

Files

  • recommender.ipynb
  • data_by_genres.csv
  • data_by_year.csv
  • descriptive_stats.csv
  • final_df.csv
  • README.md
  • Data Documentation.pdf
  • Code Docuemntation.pdf

Source Documentation:

Full credit to all sources consulted/used in this project.

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