About This Dataset

This Spotify ML Practice Dataset is designed for beginners to explore, practice, and build machine learning models, including regression, classification, and feature engineering workflows. Originally comprising 24 raw features, the dataset now has 158 engineered features and 114,000+ records, making it a comprehensive resource for end-to-end ML pipelines.

Overview

• Original Shape: (114,000 rows, 24 columns)
• Engineered Shape: (113,999 rows, 158 columns)
• Recorded Tracks: 114,000 Spotify songs

Primary Goals:

1. Demonstrate a full ML pipeline from raw data to model-ready format
2. Provide rich, diverse features for regression (popularity prediction) and classification (genre prediction)
3. Enable hands-on practice with tasks such as EDA, feature engineering, feature selection, and model building

Dataset Details

Note: A total of 114 features for genre were generated via pd.get_dummies(df['track_genre'], prefix='is_genre_'), dramatically increasing feature dimensionality to allow one-hot encoded classification tasks.

Column Engineering & Selection

1. Dropped Columns: index, album_name, track_name, duration_ms, duration_min, key, artists, track_id to remove identifiers and redundant data.
2. Type Conversions: Converted explicit from boolean to integer for modelling.
3. Combined Features: Merged key and mode into a composite key_mode integer to capture musical structure.
4. One-Hot Encoding: Expanded track_genre into 114 is_genre_ columns to handle high cardinality categorical data.
5. Label Encoding: Added track_genre_le for multiclass classification practice.
6. Custom Feature Creation: Engineered dozens of metrics (e.g., loudness_intensity, mood_pca) to enrich model inputs.
7. Clustering: Identified acoustic-mood clusters for unsupervised/semi-supervised learning.

Usage

• Regression: Predict popularity using audio/structural/custom features.
• Classification: Predict music genre via one-hot features or label encoding.
• Clustering & Unsupervised Learning: Explore mood clusters and feature groupings.
• Feature Engineering Practice: Extend dataset with new metrics (e.g., spectral features, text-based lyrics analysis).
• Full ML Pipelines: Develop end-to-end workflows including EDA, preprocessing, modelling, and evaluation.

Previous Work & Notebooks

1. What Makes a Track Popular? (Part 1)
2. Short Hits to Long Epics (Part 2)
3. Structured Feature Engineering (Part 3)
4. Execute Feature Engineering (Part 4)
5. Feature Selection (Part 5)
6. ML Pipeline (Part 6)
7. More notebooks are coming. Check out the code section for more details

Description for Spotify Songs Dataset on Kaggle

Dataset Title: Spotify Songs Dataset

Description: This dataset contains a collection of songs fetched from the Spotify API, covering various genres including "acoustic", "afrobeat", "alt-rock", "alternative", "ambient", "anime", "black-metal", "bluegrass", "blues", "bossanova", "brazil", "breakbeat", "british", "cantopop", "chicago-house", "children", "chill", "classical", "club", "comedy", "country", "dance", "dancehall", "death-metal", "deep-house", "detroit-techno", "disco", "disney", "drum-and-bass", "dub", "dubstep", "edm", "electro", "electronic", "emo", "folk", "forro", "french", "funk", "garage", "german", "gospel", "goth", "grindcore", "groove", "grunge", "guitar", "happy", "hard-rock", "hardcore", "hardstyle", "heavy-metal", "hip-hop", "holidays", "honky-tonk", "house", "idm", "indian", "indie", "indie-pop", "industrial", "iranian", "j-dance", "j-idol", "j-pop", "j-rock", "jazz", "k-pop", "kids", "latin", "latino", "malay", "mandopop", "metal", "metal-misc", "metalcore", "minimal-techno", "movies", "mpb", "new-age", "new-release", "opera", "pagode", "party", "philippines-opm", "piano", "pop", "pop-film", "post-dubstep", "power-pop", "progressive-house", "psych-rock", "punk", "punk-rock", "r-n-b", "rainy-day", "reggae", "reggaeton", "road-trip", "rock", "rock-n-roll", "rockabilly", "romance", "sad", "salsa", "samba", "sertanejo", "show-tunes", "singer-songwriter", "ska", "sleep", "songwriter", "soul", "soundtracks", "spanish", "study", "summer", "swedish", "synth-pop", "tango", "techno", "trance", "trip-hop", "turkish", "work-out", "world-music". Each entry in the dataset provides detailed information about a song, including its name, artists, album, popularity, duration, and whether it is explicit.

Data Collection Method: The data was collected using the Spotify Web API through a Python script. The script performed searches for different genres and retrieved the top tracks for each genre. The fetched data was then compiled and saved into a CSV file.

Columns Description: id: Unique identifier for the track on Spotify. name: Name of the track. genre: genre of the song. artists: Names of the artists who performed the track, separated by commas if there are multiple artists. album: Name of the album the track belongs to. popularity: Popularity score of the track (0-100, where higher is more popular). duration_ms: Duration of the track in milliseconds. explicit: Boolean indicating whether the track contains explicit content.

Potential Uses: This dataset can be used for a variety of purposes, including but not limited to:

• Music Analysis: Analyze the popularity and characteristics of songs across different genres.
• Recommendation Systems: Develop and test music recommendation algorithms.
• Trend Analysis: Study trends in music preferences and popularity over time.
• Machine Learning: Train machine learning models for tasks like genre classification or popularity prediction. _ Acknowledgements: This dataset was created using the Spotify Web API. Special thanks to Spotify for providing access to their extensive music library through their API. _ License: This dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to use, modify, and distribute this dataset, provided you give appropriate credit to the original creator. _

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.

About Dataset

Overview:

This dataset provides detailed metadata and audio analysis for a wide collection of Spotify music tracks across various genres. It includes track-level information such as popularity, tempo, energy, danceability, and other musical features that can be used for music recommendation systems, genre classification, or trend analysis. The dataset is a rich source for exploring music consumption patterns and user preferences based on song characteristics.

Dataset Details:

This dataset contains rows of individual music tracks, each described by both metadata (such as track name, artist, album, and genre) and quantitative audio features. These features reflect different musical attributes such as energy, acousticness, instrumentalness, valence, and more, making it ideal for audio machine learning projects and exploratory data analysis.

Schema and Column Descriptions:

Key Features:

• Comprehensive Track Data: Metadata combined with detailed audio analysis.
• Genre Diversity: Includes tracks from various music genres.
• Audio Feature Rich: Suitable for audio classification, recommendation engines, or clustering.
• Machine Learning Friendly: Clean and numerical format ideal for ML models.

Usage:

This dataset is valuable for:

• 🎵 Music Recommendation Systems: Building collaborative or content-based recommenders.
• 📊 Data Visualization & Dashboards: Analyzing genre or mood trends over time.
• 🤖 Machine Learning Projects: Predicting song popularity or clustering similar tracks.
• 🧠 Music Psychology & Behavioral Studies: Exploring how music features relate to emotions or behavior.

Data Maintenance:

• Source: https://huggingface.co/datasets/ConquestAce/spotify-songs
• Last Updated: 2025/04/26
• License: Check original source for usage terms.

Additional Notes:

• This dataset can be enhanced by merging it with user listening behavior data, lyrics datasets, or chart positions for more advanced analysis.
• Some columns like key, mode, and explicit may need to be mapped for better readability in visualization.

Explore a dataset of 60,000 Spotify tracks across six languages: English, Hindi, Tamil, Telugu, Malayalam, and Korean

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.

Dataset Card for Spotify Million Song Dataset

  Dataset Summary

This is Spotify Million Song Dataset. This dataset contains song names, artists names, link to the song and lyrics. This dataset can be used for recommending songs, classifying or clustering songs.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data… See the full description on the dataset page: https://huggingface.co/datasets/vishnupriyavr/spotify-million-song-dataset.

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.

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.

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 2. mysql -u -p < spotifydbdumpshare.sql

• - - - - 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

Deteccion de pelotas en tiempo real para cualquier cosa

Context

Dataset contains a comprehensive list of the most famous songs and most streamed songs as listed on Spotify.

It provides insights into each song's

• Attributes
• Popularity
• Presence on various music platforms

The dataset includes information such as track name

• Artist's name
• Release date
• Spotify playlists and charts
• Streaming statistics
• Apple Music presence
• Deezer presence
• Shazam charts
• Various audio features

Sportify Dataset7

## Overview

Sportify Dataset7 is a dataset for object detection tasks - it contains Player Ball Fastball 9kNN J1Wq CwGE JesS annotations for 4,186 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

On October 22, 2024, 'APT.' by ROSÉ and Bruno Mars was the most-streamed track on Spotify with 14.6 million streams worldwide, followed by 'Die With A Smile" by Lady Gaga and Bruno Mars, reaching over 11 million Spotify streams on Spotify that day. Billie Eilish's 'BIRDS OF A FEATHER' came third with just over 7.6 million streams. How do music artists get so many streams on Spotify? Firstly, Spotify is one of the most successful and popular music streaming services in the United States, and as of the first half of 2018 had the biggest share of music streaming subscribers in the world. With Spotify’s vast audience, featuring on the platform is a good start for emerging and popular artists hoping to make an impact. Secondly, there is no exact science to ‘going viral’. From the famous egg photo on Instagram posted in early 2019 to wildly successful music video ‘Gangnam Style’ released back in 2012, viral content comes in all shapes and sizes. Purposeful viral marketing is one way in which something could go viral, and is one of the reasons why some songs have so many streams in a short space of time. This type of marketing involves a tactical approach and pre-planning in an attempt to push the content into the public eye and encourage it to spread as quickly as possible. However, many artists who go viral do not expect to. Accessible, catchy content created by an already popular artist is already poised to do well, i.e. the latest song or album from U.S. singer Drake. This is an example of incidental viral marketing, when content spreads by itself partially as a result of an established and engaged audience. Indeed, Spotify’s most-streamed tracks generally originate from a well-known figure with a large following. But for smaller or entirely unknown content creators, going viral or experiencing their 15 minutes of fame can simply be a case of posting the right thing at the right time.

💁‍♀️Please take a moment to carefully read through this description and metadata to better understand the dataset and its nuances before proceeding to the Suggestions and Discussions section.

Dataset Description:

This dataset compiles the tracks from Spotify's official "Top Tracks of 2023" playlist, showcasing the most popular and influential music of the year according to Spotify's streaming data. It represents a wide range array of genres, artists, and musical styles that have defined the musical landscapes of 2023. Each track in the dataset is detailed with a variety of features, popularity, and metadata. This dataset serves as an excellent resource for music enthusiasts, data analysts, and researchers aiming to explore music trends or develop music recommendation systems based on empirical data.

Data Collection and Processing:

Obtaining the Data:

The data was obtained directly from the Spotify Web API, specifically from the "Top Tracks of 2023" official playlist curated by Spotify. The Spotify API provides detailed information about tracks, artists, and albums through various endpoints.

Data Processing:

To process and structure the data, I developed Python scripts using data science libraries such as pandas for data manipulation and spotipy for API interactions specifically for Spotify data retrieval.

Workflow:

1. Authentification
2. API Requests
3. Data Cleaning and Transformation
4. Saving the Data

Attribute Descriptions:

• artist_name: the artist name
• track_name: the title of the track
• is_explicit: Indicates whether the track contains explicit content
• album_release_date: The date when the track was released
• genres: A list of genres associated with the track's artist(s)
• danceability: A measure from 0.0 to 1.0 indicating how suitable a track is for dancing based on a combination of musical elements
• valence: A measure from 0.0 to 1.0 indicating the musical positiveness conveyed by a track
• energy: A measure from 0.0 to 1.0 representing a perceptual measure of intensity and activity
• loudness: The overall loudness of a track in decibels (dB)
• acousticness: A measure from 0.0 to 1.0 whether the track is acoustic.
• instrumentalness: Predicts whether a track contains no vocals
• liveness: Detects the presence of an audience in the recordings
• speechiness: Detects the presence of spoken words in a track
• key: The key the track is in. Integers map to pitches using standard Pitch Class notation.
• tempo: The overall estimated tempo of a track in beats per minute (BPM)
• mode: Modality of the track
• duration_ms: The length of the track in milliseconds
• time_signature: An estimated overall time signature of a track
• popularity: A score between 0 and 100, with 100 being the most popular

Possible Data Projects

• Trends Analysis
• Genre Popularity
• Mood and Music
• Comparison with other tracks

Disclaimer and Responsible Use:

• This dataset, derived from Spotify's "Top Tracks of 2023" playlist, is intended for educational, research, and analysis purposes only. Users are urged to use this data responsibly and ethically.
• Users should comply with Spotify's Terms of Service and Developer Policies when using this dataset.
• The dataset includes music track information such as names and artist details, which are subject to copyright. While the dataset presents this information for analytical purposes, it does not convey any rights to the music itself.
• Users of the dataset must ensure that their use does not infringe on the rights of copyright holders. Any analysis, distribution, or derivative work should respect the intellectual property rights of all parties and comply with applicable laws.
• The dataset is provided "as is," without warranty, and the creator disclaims any legal liability for the use of the dataset by others. Users are responsible for ensuring their use of the dataset is legal and ethical.
• For the most accurate and up-to-date information regarding Spotify's music, playlists, and policies, users are encouraged to refer directly to Spotify's official website. This ensures that users have access to the latest details directly from the source.
• The creator/maintainer of this dataset is not affiliated with Spotify, any third-party entities, or artists mentioned within the dataset. This project is independent and has not been authorized, sponsored, or otherwise approved by Spotify or any other mentioned entities.

Contribution

I encourage users who discover new insights, propose dataset enhancements, or craft analytics that illuminate aspects of the dataset's focus to share their findings with the community. - Kaggle Notebooks: To facilitate sharing and collaboration, users are encouraged to create and share their analyses through Kaggle notebooks. For ease of use, start your notebook by clicking "New Notebook" atop this dataset’s page on K...

This dataset, "Spotify's Long Hits (2014-2024) 🎶," offers a unique collection of over 800 tracks, each standing out for its extended playtime, marking the years from 2014 to 2024. It serves as a unique lens through which the evolution of musical duration and listener preferences can be observed over a significant period. Each track in this dataset not only surpasses the conventional lengths but also encapsulates the essence of its time, making it a valuable resource for in-depth musical analysis.

Data Science Applications: The dataset's structure lends itself to various analytical pursuits within the data science realm. Researchers and enthusiasts can delve into trend analysis to uncover shifts in musical durations over the years, perform genre-based studies to explore the relationship between genre and track length, or even train machine learning models to predict track popularity based on various features. However, make sure to use the dataset only for educational purposes as per Spotify guidelines.

Column Descriptors: - ID: The unique identifier for each track on Spotify, facilitating direct access to the track. - Name: The title of the track, revealing its identity. - Duration (Minutes): The length of each track, provided in minutes, highlighting the extended nature of these compositions. - Artists: The names of the artists involved, offering insights into the collaborative landscape of each piece.

Ethically Mined Data: This dataset has been compiled with strict adherence to ethical data mining practices, utilizing Spotify's public API in full compliance with their guidelines. It represents a harmonious blend of technology and creativity, showcasing the vast musical archive that Spotify offers.

Gratitude is extended to Spotify for the data provided and the usage of their logo in the dataset thumbnail, which adds a recognizable visual cue to this academic resource. This dataset stands as a testament to the power of music and data combined, inviting exploration into the depths of musical analysis.

This dataset contains information about songs available on Spotify, including various attributes such as title, artist, genre, release year, beats per minute, energy, danceability, loudness, liveness, valence, length, acousticness, speechiness, and popularity. The data was collected from Spotify's API and curated for analysis and research purposes and also some data is fetched from the previous data set available on kaggle data set.

Features:

title: The title of the song. artist: The artist(s) of the song. top genre: The primary genre of the song. year: The release year of the song.

Sportify Dataset4

## Overview

Sportify Dataset4 is a dataset for object detection tasks - it contains Player Ball Fastball 9kNN annotations for 1,845 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This is a dataset consisting of features for tracks fetched using Spotify's Web API. The tracks are labeled '1' or '0' ('Hit' or 'Flop') depending on some criterias of the author. This dataset can be used to make a classification model that predicts whethere a track would be a 'Hit' or not. (Note: The author does not objectively considers a track inferior, bad or a failure if its labeled 'Flop'. 'Flop' here merely implies that it is a track that probably could not be considered popular in the mainstream.) Here's an implementation of this idea in the form of a website that I made. {http://www.hitpredictor.in/}