Baselight

Spotify: Winner Tracks Audio Features🎹

Audio features and info about all the songs we've loved (2018 - 2022).

@kaggle.sejungjenn_spotify_best_songs_of_2022

About this Dataset

Spotify: Winner Tracks Audio Features🎹

🎹What is the 'recipe' for winning tracks?🤔

We sometimes wonder what makes a track successful these days?
With this question in mind, I gathered various information and audio features – danceability, speechiness or liveness – of the top tracks in the last 5 years.

Audio Features:

Mood: Danceability, Valence, Energy, Tempo
Properties: Loudness, Speechiness, Instrumentalness
Context: Liveness, Acousticness

Column Description:

  • track_uri: track's unique resource indicator code on Spotify
  • track: track name
  • artist: the artist name who performed the track
  • artist_popularity: the artist's popularity is given a value between 0 and 100, with 100 being the most popular
  • followers: the total number of followers who follow the artist's Spotify page
  • artist_genre: the genre in which the artist belongs
  • track_popularity: the track's popularity is given a value between 0 and 100, with 100 being the most popular
  • album: album name
  • year: year
  • danceability: how suitable the song is for dancing
  • valence: a value from 0.0 to 1.0 describing the positiveness with higher valence indicates happy, cheerful, euphoric mood
  • energy: a perceptual measure of intensity and activity
  • tempo: the overall estimated tempo of a track in beats per minute (BPM)
  • loudness: the overall loudness of a track in decibels (dB), ranging between -60 and 0 db.
  • speechiness: the higher the value, the more spoken words the song contains
  • instrumentalness: the higher the value, the song contains fewer spoken word vocals
  • liveness: the probability that the song was recorded with a live audience
  • acousticness: a measure from 0.0 to 1.0 of whether the track is acoustic

In-depth explanation about the audio features could be found here

Data Collection:

You can find the data collection process in the public notebook section, here.

Usage:

Discussed the usage of this dataset with fellow Kagglers, and their suggestions could be found here.

Acknowledgement

image credits: istockphoto

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