38 datasets found
  1. Spotify's premium subscribers 2015-2024

    • statista.com
    • ai-chatbox.pro
    Updated Mar 21, 2025
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    Statista (2025). Spotify's premium subscribers 2015-2024 [Dataset]. https://www.statista.com/statistics/244995/number-of-paying-spotify-subscribers/
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    Dataset updated
    Mar 21, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    How many paid subscribers does Spotify have? As of the fourth quarter of 2024, Spotify had 263 million premium subscribers worldwide, up from 236 million in the corresponding quarter of 2023. Spotify’s subscriber base has increased dramatically in the last few years and has more than doubled since early 2019. Spotify and competitors Spotify is a music streaming service originally founded in 2006 in Sweden. The platform can be used from various devices and allows users to browse through a catalogue of music licensed through multiple record labels, as well as creating and sharing playlists with other users. Additionally, listeners are able to enjoy music for free with advertisements or are also given the option to purchase a subscription to allow for unlimited ad-free music streaming. Spotify’s largest competitors are Pandora, a company that offers a similar service and remains popular in the United States, and Apple Music, which was launched in 2015. While Pandora was once among the highest-grossing music apps in the Apple App Store, recent rankings show that global services like QQ Music, NetEase Cloud Music, and YouTube Music now generate higher monthly revenues.Users are also able to register Spotify accounts using Facebook directly through the website using an app. This enables them to connect with other Facebook friends and explore their music tastes and playlists. Spotify is a popular source for keeping up-to-date with music, and the ability to enjoy Spotify anywhere at any time allows consumers to shape their music consumption around their lifestyles and preferences.

  2. Playlist2vec: Spotify Million Playlist Dataset

    • zenodo.org
    • data.niaid.nih.gov
    bin
    Updated Jun 22, 2021
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    Piyush Papreja; Piyush Papreja (2021). Playlist2vec: Spotify Million Playlist Dataset [Dataset]. http://doi.org/10.5281/zenodo.5002584
    Explore at:
    binAvailable download formats
    Dataset updated
    Jun 22, 2021
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Piyush Papreja; Piyush Papreja
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset was created using Spotify developer API. It consists of user-created as well as Spotify-curated playlists.
    The dataset consists of 1 million playlists, 3 million unique tracks, 3 million unique albums, and 1.3 million artists.
    The data is stored in a SQL database, with the primary entities being songs, albums, artists, and playlists.
    Each of the aforementioned entities are represented by unique IDs (Spotify URI).
    Data is stored into following tables:

    • album
    • artist
    • track
    • playlist
    • track_artist1
    • track_playlist1

    album

    | id | name | uri |

    id: Album ID as provided by Spotify
    name: Album Name as provided by Spotify
    uri: Album URI as provided by Spotify


    artist

    | id | name | uri |

    id: Artist ID as provided by Spotify
    name: Artist Name as provided by Spotify
    uri: Artist URI as provided by Spotify


    track

    | id | name | duration | popularity | explicit | preview_url | uri | album_id |

    id: Track ID as provided by Spotify
    name: Track Name as provided by Spotify
    duration: Track Duration (in milliseconds) as provided by Spotify
    popularity: Track Popularity as provided by Spotify
    explicit: Whether the track has explicit lyrics or not. (true or false)
    preview_url: A link to a 30 second preview (MP3 format) of the track. Can be null
    uri: Track Uri as provided by Spotify
    album_id: Album Id to which the track belongs


    playlist

    | id | name | followers | uri | total_tracks |

    id: Playlist ID as provided by Spotify
    name: Playlist Name as provided by Spotify
    followers: Playlist Followers as provided by Spotify
    uri: Playlist Uri as provided by Spotify
    total_tracks: Total number of tracks in the playlist.

    track_artist1

    | track_id | artist_id |

    Track-Artist association table

    track_playlist1

    | track_id | playlist_id |

    Track-Playlist association table

    - - - - - SETUP - - - - -


    The data is in the form of a SQL dump. The download size is about 10 GB, and the database populated from it comes out to about 35GB.

    spotifydbdumpschemashare.sql contains the schema for the database (for reference):
    spotifydbdumpshare.sql is the actual data dump.


    Setup steps:
    1. Create database

    - - - - - PAPER - - - - -


    The description of this dataset can be found in the following paper:

    Papreja P., Venkateswara H., Panchanathan S. (2020) Representation, Exploration and Recommendation of Playlists. In: Cellier P., Driessens K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science, vol 1168. Springer, Cham

  3. P

    Data from: MSSD Dataset

    • paperswithcode.com
    Updated May 13, 2025
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    Brian Brost; Rishabh Mehrotra; Tristan Jehan (2025). MSSD Dataset [Dataset]. https://paperswithcode.com/dataset/mssd
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    Dataset updated
    May 13, 2025
    Authors
    Brian Brost; Rishabh Mehrotra; Tristan Jehan
    Description

    The Spotify Music Streaming Sessions Dataset (MSSD) consists of 160 million streaming sessions with associated user interactions, audio features and metadata describing the tracks streamed during the sessions, and snapshots of the playlists listened to during the sessions.

    This dataset enables research on important problems including how to model user listening and interaction behaviour in streaming, as well as Music Information Retrieval (MIR), and session-based sequential recommendations.

  4. o

    Spotify Million Playlist: Recsys Challenge 2018 Dataset

    • explore.openaire.eu
    • data.niaid.nih.gov
    • +1more
    Updated Sep 27, 2018
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    AIcrowd (2018). Spotify Million Playlist: Recsys Challenge 2018 Dataset [Dataset]. http://doi.org/10.5281/zenodo.6425593
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    Dataset updated
    Sep 27, 2018
    Authors
    AIcrowd
    Description

    Spotify Million Playlist Dataset Challenge Summary The Spotify Million Playlist Dataset Challenge consists of a dataset and evaluation to enable research in music recommendations. It is a continuation of the RecSys Challenge 2018, which ran from January to July 2018. The dataset contains 1,000,000 playlists, including playlist titles and track titles, created by users on the Spotify platform between January 2010 and October 2017. The evaluation task is automatic playlist continuation: given a seed playlist title and/or initial set of tracks in a playlist, to predict the subsequent tracks in that playlist. This is an open-ended challenge intended to encourage research in music recommendations, and no prizes will be awarded (other than bragging rights). Background Playlists like Today’s Top Hits and RapCaviar have millions of loyal followers, while Discover Weekly and Daily Mix are just a couple of our personalized playlists made especially to match your unique musical tastes. Our users love playlists too. In fact, the Digital Music Alliance, in their 2018 Annual Music Report, state that 54% of consumers say that playlists are replacing albums in their listening habits. But our users don’t love just listening to playlists, they also love creating them. To date, over 4 billion playlists have been created and shared by Spotify users. People create playlists for all sorts of reasons: some playlists group together music categorically (e.g., by genre, artist, year, or city), by mood, theme, or occasion (e.g., romantic, sad, holiday), or for a particular purpose (e.g., focus, workout). Some playlists are even made to land a dream job, or to send a message to someone special. The other thing we love here at Spotify is playlist research. By learning from the playlists that people create, we can learn all sorts of things about the deep relationship between people and music. Why do certain songs go together? What is the difference between “Beach Vibes” and “Forest Vibes”? And what words do people use to describe which playlists? By learning more about nature of playlists, we may also be able to suggest other tracks that a listener would enjoy in the context of a given playlist. This can make playlist creation easier, and ultimately help people find more of the music they love. Dataset To enable this type of research at scale, in 2018 we sponsored the RecSys Challenge 2018, which introduced the Million Playlist Dataset (MPD) to the research community. Sampled from the over 4 billion public playlists on Spotify, this dataset of 1 million playlists consist of over 2 million unique tracks by nearly 300,000 artists, and represents the largest public dataset of music playlists in the world. The dataset includes public playlists created by US Spotify users between January 2010 and November 2017. The challenge ran from January to July 2018, and received 1,467 submissions from 410 teams. A summary of the challenge and the top scoring submissions was published in the ACM Transactions on Intelligent Systems and Technology. In September 2020, we re-released the dataset as an open-ended challenge on AIcrowd.com. The dataset can now be downloaded by registered participants from the Resources page. Each playlist in the MPD contains a playlist title, the track list (including track IDs and metadata), and other metadata fields (last edit time, number of playlist edits, and more). All data is anonymized to protect user privacy. Playlists are sampled with some randomization, are manually filtered for playlist quality and to remove offensive content, and have some dithering and fictitious tracks added to them. As such, the dataset is not representative of the true distribution of playlists on the Spotify platform, and must not be interpreted as such in any research or analysis performed on the dataset. Dataset Contains 1000 examples of each scenario: Title only (no tracks) Title and first track Title and first 5 tracks First 5 tracks only Title and first 10 tracks First 10 tracks only Title and first 25 tracks Title and 25 random tracks Title and first 100 tracks Title and 100 random tracks Download Link Full Details: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge Download Link: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge/dataset_files {"references": ["C.W. Chen, P. Lamere, M. Schedl, and H. Zamani. Recsys Challenge 2018: Automatic Music Playlist Continuation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys '18), 2018."]}

  5. Data from: Spotify Playlists Dataset

    • zenodo.org
    • explore.openaire.eu
    • +1more
    zip
    Updated Jan 24, 2020
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    Martin Pichl; Eva Zangerle; Eva Zangerle; Martin Pichl (2020). Spotify Playlists Dataset [Dataset]. http://doi.org/10.5281/zenodo.2594557
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Martin Pichl; Eva Zangerle; Eva Zangerle; Martin Pichl
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description


    This dataset is based on the subset of users in the #nowplaying dataset who publish their #nowplaying tweets via Spotify. In principle, the dataset holds users, their playlists and the tracks contained in these playlists.

    The csv-file holding the dataset contains the following columns: "user_id", "artistname", "trackname", "playlistname", where

    • user_id is a hash of the user's Spotify user name
    • artistname is the name of the artist
    • trackname is the title of the track and
    • playlistname is the name of the playlist that contains this track.

    The separator used is , each entry is enclosed by double quotes and the escape character used is \.

    A description of the generation of the dataset and the dataset itself can be found in the following paper:

    Pichl, Martin; Zangerle, Eva; Specht, Günther: "Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name?" in 15th IEEE International Conference on Data Mining Workshops (ICDM 2015), pp. 1360-1365, IEEE, Atlantic City, 2015.

  6. Spotify Dataset

    • brightdata.com
    .json, .csv, .xlsx
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    Bright Data, Spotify Dataset [Dataset]. https://brightdata.com/products/datasets/spotify
    Explore at:
    .json, .csv, .xlsxAvailable download formats
    Dataset authored and provided by
    Bright Datahttps://brightdata.com/
    License

    https://brightdata.com/licensehttps://brightdata.com/license

    Area covered
    Worldwide
    Description

    Gain valuable insights into music trends, artist popularity, and streaming analytics with our comprehensive Spotify Dataset. Designed for music analysts, marketers, and businesses, this dataset provides structured and reliable data from Spotify to enhance market research, content strategy, and audience engagement.

    Dataset Features

    Track Information: Access detailed data on songs, including track name, artist, album, genre, and release date. Streaming Popularity: Extract track popularity scores, listener engagement metrics, and ranking trends. Artist & Album Insights: Analyze artist performance, album releases, and genre trends over time. Related Searches & Recommendations: Track related search terms and suggested content for deeper audience insights. Historical & Real-Time Data: Retrieve historical streaming data or access continuously updated records for real-time trend analysis.

    Customizable Subsets for Specific Needs Our Spotify Dataset is fully customizable, allowing you to filter data based on track popularity, artist, genre, release date, or listener engagement. Whether you need broad coverage for industry analysis or focused data for content optimization, we tailor the dataset to your needs.

    Popular Use Cases

    Market Analysis & Trend Forecasting: Identify emerging music trends, genre popularity, and listener preferences. Artist & Label Performance Tracking: Monitor artist rankings, album success, and audience engagement. Competitive Intelligence: Analyze competitor music strategies, playlist placements, and streaming performance. AI & Machine Learning Applications: Use structured music data to train AI models for recommendation engines, playlist curation, and predictive analytics. Advertising & Sponsorship Insights: Identify high-performing tracks and artists for targeted advertising and sponsorship opportunities.

    Whether you're optimizing music marketing, analyzing streaming trends, or enhancing content strategies, our Spotify Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

  7. s

    Distribution Of Spotify Monthly Active Users By Region

    • searchlogistics.com
    Updated Apr 1, 2025
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    (2025). Distribution Of Spotify Monthly Active Users By Region [Dataset]. https://www.searchlogistics.com/learn/statistics/spotify-statistics/
    Explore at:
    Dataset updated
    Apr 1, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    34% of Spotify’s monthly active users live in Europe. That means that Spotify has 147.22 million users in the EU regions alone. Here’s the breakdown of regions that contribute the most users to Spotify:

  8. c

    Spotify Playlist ORIGINS Dataset

    • cubig.ai
    Updated Jun 5, 2025
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    CUBIG (2025). Spotify Playlist ORIGINS Dataset [Dataset]. https://cubig.ai/store/products/402/spotify-playlist-origins-dataset
    Explore at:
    Dataset updated
    Jun 5, 2025
    Dataset authored and provided by
    CUBIG
    License

    https://cubig.ai/store/terms-of-servicehttps://cubig.ai/store/terms-of-service

    Measurement technique
    Synthetic data generation using AI techniques for model training, Privacy-preserving data transformation via differential privacy
    Description

    1) Data Introduction • The Spotify Playlist-ORIGINS Dataset is a dataset of Spotify playlists called ORIGINS, which individuals have made with their favorite songs since 2014.

    2) Data Utilization (1) Spotify Playlist-ORIGINS Dataset has characteristics that: • This dataset contains detailed music information for each playlist, including song name, artist, album, genre, release year, track ID, and structured metadata such as name, description, and song order for each playlist. (2) Spotify Playlist-ORIGINS Dataset can be used to: • Playlist-based music recommendation and user preference analysis: It can be used to develop a machine learning/deep learning-based music recommendation system or to study user preference analysis using playlist and song information. • Music Trend and Genre Popularity Analysis: It analyzes release year, genre, and artist data and can be used to study the music industry and culture, including music trends by period and genre, and changes in popular artists and songs.

  9. h

    spotify-tracks-dataset

    • huggingface.co
    Updated Jun 30, 2023
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    maharshipandya (2023). spotify-tracks-dataset [Dataset]. https://huggingface.co/datasets/maharshipandya/spotify-tracks-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 30, 2023
    Authors
    maharshipandya
    License

    https://choosealicense.com/licenses/bsd/https://choosealicense.com/licenses/bsd/

    Description

    Content

    This is a dataset of Spotify tracks over a range of 125 different genres. Each track has some audio features associated with it. The data is in CSV format which is tabular and can be loaded quickly.

      Usage
    

    The dataset can be used for:

    Building a Recommendation System based on some user input or preference Classification purposes based on audio features and available genres Any other application that you can think of. Feel free to discuss!

      Column… See the full description on the dataset page: https://huggingface.co/datasets/maharshipandya/spotify-tracks-dataset.
    
  10. Million Song Data Analysis 2

    • kaggle.com
    Updated Jun 29, 2024
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    Zirian Afandy (2024). Million Song Data Analysis 2 [Dataset]. https://www.kaggle.com/datasets/ziriantahirli/million-song-data-analysis-2
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 29, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Zirian Afandy
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Did We Solve the Problem? The objective of this analysis was to predict high streaming counts on Spotify and perform a detailed cluster analysis to understand user behavior. Here’s a summary of how we addressed each part of the objective:

    Prediction of High Streaming Counts:

    Implemented Multiple Models: We utilized several machine learning models including Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). Comparison and Evaluation: These models were evaluated based on classification metrics like accuracy, precision, recall, and F1-score. The Gradient Boosting and Random Forest models were found to be the most effective in predicting high streaming counts. Cluster Analysis:

    K-means Clustering: We applied K-means clustering to segment users into three clusters based on their listening behavior. Detailed Characterization: Each cluster was analyzed to understand the distinct characteristics, such as average playtime, skip rate, offline usage, and shuffle usage. Visualizations: Histograms and scatter plots were used to visualize the distributions and relationships within each cluster. Results and Insights Effective Models: The Gradient Boosting and Random Forest models provided the highest accuracy and balanced performance for predicting high streaming counts. User Segmentation: The cluster analysis revealed three distinct user segments: Cluster 1: Users with longer playtimes and lower skip rates. Cluster 2: Users with moderate playtimes and skip rates. Cluster 3: Users with shorter playtimes and higher skip rates. These insights can be leveraged for targeted marketing, personalized recommendations, and improving user engagement on Spotify.

    Conclusion Yes, we solved the problem. We successfully predicted high streaming counts using effective machine learning models and provided a detailed cluster analysis to understand user behavior. The analysis offers valuable insights for enhancing Spotify’s recommendation system and user experience.

  11. Spotify Streaming Data

    • kaggle.com
    Updated Oct 26, 2023
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    Anshul Raj Verma (2023). Spotify Streaming Data [Dataset]. https://www.kaggle.com/datasets/arvanshul/spotify-streaming-history-and-playlist-data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 26, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Anshul Raj Verma
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Description

    This is my Spotify Streaming History and Playlist data which I was got from Spotify itself by requesting it.

    This dataset contains two streaming history JSON files and a Playlist JSON file.

    Usage: This data is used for EDA and draw some insights about the user like his favourite artists, songs or albums.

  12. s

    Spotify Monthly Active Users

    • searchlogistics.com
    Updated Apr 1, 2025
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    (2025). Spotify Monthly Active Users [Dataset]. https://www.searchlogistics.com/learn/statistics/spotify-statistics/
    Explore at:
    Dataset updated
    Apr 1, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    As of January 2025, Spotify has over 640 million monthly active users. Here is the full breakdown of Spotify users by year since 2015:

  13. Spotify_1Million_Tracks

    • kaggle.com
    Updated Jun 21, 2023
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    Amitansh joshi (2023). Spotify_1Million_Tracks [Dataset]. http://doi.org/10.34740/kaggle/dsv/5987852
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    Kaggle
    Authors
    Amitansh joshi
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Description

    This dataset was extracted from the Spotify platform using the Python library "Spotipy", which allows users to access music data provided via APIs. The dataset collected includes about 1 Million tracks with 19 features between 2000 and 2023. Also, there is a total of 61,445 unique artists and 82 genres in the data.

    This clean data has been prepared and utilized for research purposes. Its significance lies in its potential to unravel patterns and predict song popularity prior to its release. This dataset could be used to create various predictive models with machine-learning/deep-learning techniques.

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5006553%2F317c91feba2aa097559fd59bf408e944%2Ftable_desc_spot.png?generation=1687699189506270&alt=media" alt="">

  14. o

    Google Play Store Spotify User Reviews

    • opendatabay.com
    .undefined
    Updated Jul 3, 2025
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    Datasimple (2025). Google Play Store Spotify User Reviews [Dataset]. https://www.opendatabay.com/data/dataset/38b8af43-8609-485a-b332-0d8257e530ec
    Explore at:
    .undefinedAvailable download formats
    Dataset updated
    Jul 3, 2025
    Dataset authored and provided by
    Datasimple
    Area covered
    Reviews & Ratings
    Description

    This dataset contains customer reviews and ratings for the Spotify application, collected directly from the Google Play Store. It offers insight into user experiences and satisfaction with one of the world's largest music streaming services, which serves over 422 million monthly active users [1]. The data provides a valuable resource for understanding public sentiment towards the application and identifying factors that contribute to user satisfaction or dissatisfaction [1]. The data was collected by scraping Spotify reviews from the Google Play Store [1].

    Columns

    • Time_submitted: Records the specific time and date when a review was submitted by a user [2].
    • Review: Contains the full text of the user's review [2].
    • Rating: Represents the numerical score given by the user, ranging from 1 to 5, indicating their satisfaction level [2].
    • Total_thumbsup: Indicates how many other users found the particular review helpful [2].
    • Reply: Shows any direct reply to the user's review, often from the application developer or support [2].

    Distribution

    The dataset comprises 61,594 rows (or records) of Spotify app reviews [2]. The data is typically available in a CSV file format [3]. Review ratings range from 1 to 5, with 22,095 reviews rated 4.80 - 5.00, and 17,653 reviews rated 1.00 - 1.20 [4]. The number of 'thumbs up' for reviews ranges from 0 to 8195, with the vast majority (61,378 reviews) having between 0 and 409.75 thumbs up [5].

    Usage

    This dataset is ideal for various analytical applications, including: * Sentiment analysis to gauge overall user sentiment towards the Spotify app [1]. * Identifying key themes and reasons behind 1-star and 5-star reviews to understand user satisfaction drivers and pain points [1]. * Analysing trends in user feedback over time. * Developing machine learning models for text classification or natural language processing (NLP). * Understanding the impact of app updates or changes on user perception.

    Coverage

    The dataset covers reviews submitted between 1st January 2022 and 9th July 2022 [2]. The reviews were collected globally from the Google Play Store [2, 6]. There are no specific demographic notes on data availability, as it is based on publicly available app store reviews.

    License

    CC-BY

    Who Can Use It

    • Data Scientists and Analysts: For conducting sentiment analysis, natural language processing, and building predictive models.
    • Product Managers: To gain insights into user feedback, identify features that users appreciate or dislike, and inform product development decisions.
    • Market Researchers: To understand consumer perception of streaming services and competitive analysis.
    • Academic Researchers: For studies on user behaviour, app reviews, and digital product success.
    • App Developers: To monitor user satisfaction and identify areas for improvement in their applications.

    Dataset Name Suggestions

    • Spotify App Reviews 2022 (Google Play Store)
    • Google Play Store Spotify User Reviews
    • Spotify Mobile App Ratings and Feedback
    • Spotify User Sentiment Data
    • Global Spotify App Reviews (Jan-Jul 2022)

    Attributes

    Original Data Source: Spotify App Reviews

  15. c

    Spotify Tracks Dataset

    • cubig.ai
    Updated May 20, 2025
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    CUBIG (2025). Spotify Tracks Dataset [Dataset]. https://cubig.ai/store/products/276/spotify-tracks-dataset
    Explore at:
    Dataset updated
    May 20, 2025
    Dataset authored and provided by
    CUBIG
    License

    https://cubig.ai/store/terms-of-servicehttps://cubig.ai/store/terms-of-service

    Measurement technique
    Synthetic data generation using AI techniques for model training, Privacy-preserving data transformation via differential privacy
    Description

    1) Data Introduction • The Spotify Tracks Dataset contains information on tracks from over 125 music genres, including both audio features (e.g., danceability, energy, valence) and metadata (e.g., title, artist, genre).

    2) Data Utilization (1) Characteristics of the Spotify Tracks Dataset: • The data is structured in a tabular format at the track level, where each column represents numerical or categorical features based on musical properties. This makes it suitable for recommendation systems, genre classification, and emotion analysis. • It includes multi-dimensional attributes grounded in music theory such as track duration, time signature, energy, loudness, tempo, and speechiness—enabling its use in music classification and clustering tasks.

    (2) Applications of the Spotify Tracks Dataset: • Design of Music Recommendation Systems: It can be used to build content-based filtering systems or hybrid recommendation algorithms based on user preferences.

  16. A

    ‘Spotify Recommendation’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Spotify Recommendation’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-spotify-recommendation-3903/3a5b5131/?iid=006-678&v=presentation
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    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Spotify Recommendation’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/bricevergnou/spotify-recommendation on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    Spotify Recommandation

    ( You can check how I used this dataset on my github repository )

    I am basically a HUGE fan of music ( mostly French rap though with some exceptions but I love music ). And someday , while browsing stuff on Internet , I found the Spotify's API . I knew I had to use it when I found out you could get information like danceability about your favorite songs just with their id's.

    https://user-images.githubusercontent.com/86613710/127216769-745ac143-7456-4464-bbe3-adc53872c133.png" alt="image">

    Once I saw that , my machine learning instincts forced me to work on this project.

    1. Data Collection

    1.1 Playlist creation

    I collected 100 liked songs and 95 disliked songs

    For those I like , I made a playlist of my favorite 100 songs. It is mainly French Rap , sometimes American rap , rock or electro music.

    For those I dislike , I collected songs from various kind of music so the model will have a broader view of what I don't like

    There is : - 25 metal songs ( Cannibal Corps ) - 20 " I don't like " rap songs ( PNL ) - 25 classical songs - 25 Disco songs

    I didn't include any Pop song because I'm kinda neutral about it

    1.2 Getting the ID's

    1. From the Spotify's API "Get a playlist's Items" , I turned the playlists into json formatted data which cointains the ID and the name of each track ( ids/yes.py and ids/no.py ). NB : on the website , specify "items(track(id,name))" in the fields format , to avoid being overwhelmed by useless data.

    2. With a script ( ids/ids_to_data.py ) , I turned the json data into a long string with each ID separated with a comma.

    1.3 Getting the statistics

    Now I just had to enter the strings into the Spotify API "Get Audio Features from several tracks" and get my data files ( data/good.json and data/dislike.json )

    2. Data features

    From Spotify's API documentation :

    • acousticness : A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
    • danceability : Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
    • duration_ms : The duration of the track in milliseconds.
    • energy : Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
    • instrumentalness : Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
    • key : The key the track is in. Integers map to pitches using standard Pitch Class notation . E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on.
    • liveness : Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
    • loudness : The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
    • mode : Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
    • speechiness : Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
    • tempo : The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
    • time_signature : An estimated overall time signature of a track. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure).
    • valence : A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

    And the variable that has to be predicted :

    • liked : 1 for liked songs , 0 for disliked songs

    --- Original source retains full ownership of the source dataset ---

  17. o

    Spotify App Reviews

    • opendatabay.com
    .undefined
    Updated Jun 18, 2025
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    Datasimple (2025). Spotify App Reviews [Dataset]. https://www.opendatabay.com/data/consumer/38b8af43-8609-485a-b332-0d8257e530ec
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    .undefinedAvailable download formats
    Dataset updated
    Jun 18, 2025
    Dataset authored and provided by
    Datasimple
    Description

    Overview Spotify is one of the largest music streaming service providers, with over 422 million monthly active users, including 182 million paying subscribers, as of March 2022. Some of them don't hesitate to share their experience using this application along with the given rating to denote how satisfied they are with the Application

    The way data was collected Scraping Spotify reviews on Google Play Store

    Ideas for using this dataset Sentiment analysis What makes the application receive 1-star and 5-star

    Original Data Source: Spotify App Reviews

  18. h

    spotify-tracks-lite

    • huggingface.co
    Updated May 14, 2024
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    Anton Blu (2024). spotify-tracks-lite [Dataset]. https://huggingface.co/datasets/engels/spotify-tracks-lite
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 14, 2024
    Authors
    Anton Blu
    License

    https://choosealicense.com/licenses/bsd/https://choosealicense.com/licenses/bsd/

    Description

    Context

    This dataset consists of 24000 tracks from 30 genres, and is a shrunk version of maharshipandya/spotify-tracks-dataset dataset. All non-heuristic data is cut and cleaned for better usability and performance. All data taken from Spotify API and is open source. This dataset can be used to train prediction models based on user preferences, or categorise tracks by corresponding heuristic.

      Column Description
    

    danceability: Danceability describes how suitable a track is… See the full description on the dataset page: https://huggingface.co/datasets/engels/spotify-tracks-lite.

  19. s

    Distribution Of Spotify Paying Subscribers By Region

    • searchlogistics.com
    Updated Apr 1, 2025
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    (2025). Distribution Of Spotify Paying Subscribers By Region [Dataset]. https://www.searchlogistics.com/learn/statistics/spotify-statistics/
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    Dataset updated
    Apr 1, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The latest Spotify statistics from the company’s annual report show that 69% of Spotify premium subscribers are located in Europe and North America.

  20. s

    Spotify User Behaviour Statistics

    • searchlogistics.com
    Updated Apr 1, 2025
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    (2025). Spotify User Behaviour Statistics [Dataset]. https://www.searchlogistics.com/learn/statistics/spotify-statistics/
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    Dataset updated
    Apr 1, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    North American Spotify users spend the most time on the platform steaming an average of 140 minutes of content on the Spotify app daily.

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Statista (2025). Spotify's premium subscribers 2015-2024 [Dataset]. https://www.statista.com/statistics/244995/number-of-paying-spotify-subscribers/
Organization logo

Spotify's premium subscribers 2015-2024

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58 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Mar 21, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
Worldwide
Description

How many paid subscribers does Spotify have? As of the fourth quarter of 2024, Spotify had 263 million premium subscribers worldwide, up from 236 million in the corresponding quarter of 2023. Spotify’s subscriber base has increased dramatically in the last few years and has more than doubled since early 2019. Spotify and competitors Spotify is a music streaming service originally founded in 2006 in Sweden. The platform can be used from various devices and allows users to browse through a catalogue of music licensed through multiple record labels, as well as creating and sharing playlists with other users. Additionally, listeners are able to enjoy music for free with advertisements or are also given the option to purchase a subscription to allow for unlimited ad-free music streaming. Spotify’s largest competitors are Pandora, a company that offers a similar service and remains popular in the United States, and Apple Music, which was launched in 2015. While Pandora was once among the highest-grossing music apps in the Apple App Store, recent rankings show that global services like QQ Music, NetEase Cloud Music, and YouTube Music now generate higher monthly revenues.Users are also able to register Spotify accounts using Facebook directly through the website using an app. This enables them to connect with other Facebook friends and explore their music tastes and playlists. Spotify is a popular source for keeping up-to-date with music, and the ability to enjoy Spotify anywhere at any time allows consumers to shape their music consumption around their lifestyles and preferences.

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