This notebook uses a dataframe containing tracks from Spotify that contains features like artists, songs, danceability, loudness, acousticness etc. After various preprocessing steps, one hot encoding, normalization of numerical features, a vector for a playlist is compared to individual song vectors using cosine similarity to generate recommendations. Scores are assigned to songs. 40 songs with the highest scores are displayed as recommended songs.
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View Code? Open in Web Editor NEWCreated a Spotify recommendation system that generates a playlist of 40 recommended songs based on a user's playlist using cosine similarity and Spotify API