Name: Spotify: Winner Tracks Audio Features🎹
Creator: Kaggle
License: http://opendatacommons.org/licenses/odbl/1.0/

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

Tables

Df Complete

@kaggle.sejungjenn_spotify_best_songs_of_2022.df_complete

76.81 kB
500 rows
19 columns

CREATE TABLE df_complete (
  "track_uri" VARCHAR,
  "track" VARCHAR,
  "artist" VARCHAR,
  "artist_popularity" BIGINT,
  "followers" BIGINT,
  "artist_genre" VARCHAR,
  "track_popularity" BIGINT,
  "album" VARCHAR,
  "year" BIGINT,
  "danceability" DOUBLE,
  "valence" DOUBLE,
  "energy" DOUBLE,
  "tempo" DOUBLE,
  "loudness" DOUBLE,
  "speechiness" DOUBLE,
  "instrumentalness" DOUBLE,
  "liveness" DOUBLE,
  "acousticness" DOUBLE,
  "key" BIGINT
);