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Spotify Track Audio Features & Genres
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About
This dataset offers information about Spotify tracks, encompassing a diverse collection of 125 genres. It was compiled and cleaned using Spotify's Web API and Python. Presented in CSV format, this dataset is easily accessible for analysis. It includes multiple columns, each representing distinct audio features associated with individual tracks. Users can discern patterns across various genres and facilitate genre prediction using machine learning models based on perceptible audio nuances.
Columns
- Unnamed: 0: An index column for the records. (Integer)
- track_id: A unique identifier for each track. (String)
- artists: The name(s) of the artist(s) associated with the track. (String)
- album_name: The title of the album to which the track belongs. (String)
- track_name: The name of the specific track. (String)
- popularity: A numerical score indicating the track's popularity on Spotify, ranging from 0 to 100. (Integer)
- duration_ms: The duration of the track measured in milliseconds. (Integer)
- explicit: A boolean value indicating whether the track contains explicit content (True or False). (Boolean)
- danceability: A score from 0 to 1 representing how suitable a track is for dancing based on musical elements. (Float)
- energy: A measure of the intensity and activity within a track, ranging from 0 to 1. (Float)
- key: The musical key of the track, represented by an integer value from 0 to 11, where each number signifies a different key. (Integer)
- loudness: The loudness of the track in decibels (dB), where positive values indicate louder songs and negative values suggest quieter ones. (Float)
- mode: The tonal mode of the track, represented by an integer value (0 for minor, 1 for major). (Integer)
- speechiness: A score from 0 to 1 representing the presence of spoken words in a track. (Float)
- acousticness: A score from 0 to 1 representing how much a track leans towards acoustic sounds rather than electric ones. (Float)
- instrumentalness: A score from 0 to 1 indicating the likelihood of a track being instrumental rather than vocal-oriented. (Float)
- liveness: A score from 0 to 1 representing the presence of an audience during the recording or performance of a track. (Float)
- valence: A score from 0 to 1 measuring the musical positiveness conveyed by a track, with higher values indicating more positive or happy tracks. (Float)
- tempo: The speed or pace of a song in beats per minute (BPM). (Float)
- time_signature: The number of beats within each bar of the track. (Integer)
- track_genre: The genre of the track. (String)
Distribution
The dataset is provided in CSV format. The
train.csv file is approximately 20.12 MB in size and contains 21 columns. It includes 114,000 records, with most columns having 100% valid entries.Usage
This dataset is ideal for:
- Music Recommendation Systems: Utilise audio features like danceability, energy, and valence to build systems that suggest similar tracks or new genres based on user preferences.
- Genre Classification: Train machine learning models using audio features to accurately classify tracks into their respective genres, which can assist in organising large music libraries or creating genre-specific playlists.
- Studying Genre Evolution: Analyse trends in various audio features over time to gain insights into how different music genres have evolved, helping to understand cultural shifts and influences on genre development.
- General Data Analysis and Visualisation: Explore relationships between musical attributes and genre dynamics.
Coverage
The dataset covers a diverse collection of 125 genres. Specific geographic, time range, or demographic scopes are not detailed in the available information.
License
CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication.
Who Can Use It
This dataset is intended for:
- Aspiring Audiophiles: Those with a keen interest in music and audio characteristics.
- Music Enthusiasts: Individuals who enjoy exploring music data and its underlying features.
- Data Scientists: Professionals or learners looking to apply machine learning models for music analysis, recommendation, or classification tasks.
- Researchers: Anyone conducting studies into genre dynamics, musical attributes, and the evolution of music.
Dataset Name Suggestions
- Spotify Track Audio Features & Genres
- Music Genre Classification Dataset
- Audio Features for Music Analysis
- Global Music Characteristics
Attributes
Original Data Source: Spotify Track Audio Features & Genres
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