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Global Trending Spotify Tracks Data
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About
This collection features Spotify Tracks Acoustic Features, sourced from the latest trending playlists globally. The data was specifically gathered to aid in the creation of a simple song recommendation system, relying on metrics such as track acoustic features, artist popularity, and other crucial metadata. The file contains publicly available information retrieved via the Spotify Developer API and does not include any proprietary Spotify audio content or actual audio tracks. Collaboration is encouraged, as the underlying project has been open-sourced with a modular design.
Columns
The dataset includes 27 distinct columns, detailing both track metadata and calculated acoustic features:
- uris: Universal Resource Locators for Spotify tracks.
- names: The titles of the tracks.
- artist_names: Names of the performing artists or bands, with Taylor Swift being the most frequently occurring artist.
- artist_uris: Universal Resource Locators for the corresponding Artists on Spotify.
- artist_pop: A numerical measure of the artist's popularity at the time of data collection (mean value is 59.2).
- artist_genres: The associated genres for the artist.
- albums: The album from which the track originated.
- track_pop: The measured popularity of the track itself (mean value is 59.9).
- danceability: An assessment of how suitable the track is for dancing (mean value is 0.6).
- energy: A perceptual measure of intensity and activity (mean value is 0.58).
- keys: The estimated musical key (mean value is 5.06).
- loudness: The overall perceived loudness in decibels (dB), with a mean of -9.99.
- modes: Indicates modality (Major or Minor), with a mean of 0.66.
- speechiness: Detects the presence of spoken words in the track (mean value is 0.09).
- acousticness: A confidence measure that the track is acoustic (mean value is 0.32).
- instrumentalness: Predicts whether a track contains no vocals (mean value is 0.14).
- liveness: Detects the presence of an audience in the recording, suggesting a live performance (mean value is 0.17).
- valences: A measure of musical positive emotional tone (mean value is 0.46).
- tempos: The overall estimated tempo of the track in beats per minute (BPM), with a mean of 118 BPM.
- types: Indicates the data type, usually 'audio_features'.
- ids: Unique Spotify identifiers for the tracks.
- track_hrefs: API endpoint links to the track data.
- analysis_urls: API endpoint links for detailed audio analysis.
- durations_ms: The length of the track in milliseconds (mean duration is 211,000 ms).
- time_signatures: The estimated time signature or meter (4/4 is the most common).
- playlist_name: The name of the source playlist, examples include 'Sleep' and 'Peaceful Piano'.
Distribution
The data is available in a standard tabular format, typically provided as a CSV file (
tracks.csv). The file contains 7,994 records and features 27 distinct attributes. The data quality is high, with 100% valid values across all observed track features. The size of the core data file is approximately 3.09 MB.Usage
This data is perfectly suited for several applications, including:
- Developing and testing novel song recommendation algorithms, focusing on acoustic similarity.
- Conducting data analytics to understand correlations between track features (e.g., energy, valence) and commercial success or playlist inclusion.
- Training machine learning models for genre classification or prediction based on objective acoustic metrics.
- Exploring patterns in music characteristics across trending global playlists.
Coverage
The tracks included are sourced from the selection of the latest playlists trending across the globe. The dataset reflects the artist and track popularity metrics at the time of collection. The source material is publicly available information gathered from the Spotify platform.
License
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Who Can Use It
Intended users include data scientists looking to implement music recommendation systems, developers interested in working with the Spotify Developer API and structured audio feature data, and students engaged in academic research focused on music data analytics and machine learning.
Dataset Name Suggestions
- MIAS Spotify Acoustic Track Features
- Global Trending Spotify Tracks Data
- Spotify Song Feature Data for Recommendation Systems
Attributes
Original Data Source: Global Trending Spotify Tracks Data
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