![]() ![]() Liveness: how likely the the song was recorded in front of an audience.Purely instrumental songs score higher while spoken word and rap songs score lower. Instrumentalness: how instrumental a track is.Hard rock and punk scores higher while piano ballads score lower. Energy: how intense and active a song feels.Infectious pop songs score higher while stiff classical music scores lower. Danceability: how appropriate a song is for the dance floor, based on tempo, rhythm stability, beat strength, and overall regularity.Songs with soft pianos and violins score higher while songs with distorted guitars and screaming score lower. Spotify themselves defines each measure, but briefly: The data spans virtually every genre and features both obscure and popular tracks.Įvery song in the data set is broken down by several key musical indicators. Consequently, data on almost 170,000 songs from 1921 to 2020 were taken and made available on Kaggle. Spotify’s Web API grants developers access to their vast music library. Instead, it only needs to produce suggestions I can vet and creatively name, saving me the time of researching songs across different genres. The algorithm doesn’t need to classify every song nor does every playlist need to be perfect. I used an unsupervised learning technique to find closely related music and create its own playlists. While many users enjoy going through songs and creating their own playlists based on their own tastes, I wanted to do something different. On my home page right now, I see playlists for: Rap Caviar, Hot Country, Pump Pop, and many others that span all sorts of musical textures. Spotify presents no shortage of playlists to offer. ![]()
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