Spotify Low Popularity Music Data

Columns

• danceability: Describes how suitable a track is for dancing, with values ranging from 0.0 (least danceable) to 1.0 (most danceable).
• energy: Represents a perceptual measure of intensity and activity, such as speed, noise, and perceived loudness, ranging from 0.0 to 1.0.
• key: The estimated musical key the track is in. Integers map to pitches using standard Pitch Class notation (0 = C, 1 = C#, etc., up to 11 = B).
• loudness: The overall loudness of a track in decibels (dB), averaged across the entire track. Typical values range from -60 to 0 dB.
• mode: Indicates the modality (major or minor) of a track. Major is represented by 1, and minor by 0.
• speechiness: Detects the presence of spoken words in a track. Higher values (e.g., above 0.66) indicate speech, while values below 0.33 typically represent music and other non-speech-like tracks.
• acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic. Values closer to 1.0 indicate a high confidence the track is acoustic.
• instrumentalness: Predicts whether a track contains no vocals. Values closer to 1.0 suggest a greater likelihood of the track being instrumental.
• liveness: Detects the presence of an audience in the recording. Higher values indicate an increased probability that the track was performed live.
• valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. High valence tracks sound more positive (e.g., happy, cheerful), while low valence tracks sound more negative (e.g., sad, angry).
• duration_ms: The duration of the track in milliseconds.
• explicit: A boolean value indicating whether the song contains explicit content (true) or not (false).
• popularity: The popularity of the track, ranging from 0 to 100 on Spotify. In this dataset, values range from 0 to 9.
• track_name: The name or title of the track.
• track_artist: The name of the primary artist(s) on the track.
• track_id: The unique Spotify ID for the track, which can be used to identify it within the Spotify platform.

Distribution

Usage

• Music analysis and research: Gaining insights into the audio characteristics that distinguish unpopular songs from others.
• Machine learning model development: Training algorithms for music recommendation systems, genre classification, or predicting song popularity based on audio features.
• Data exploration: Identifying hidden patterns, trends, or clusters within the audio attributes of less-listened-to tracks.
• Educational purposes: Providing a real-world dataset for teaching data science, audio processing, or music information retrieval concepts.

Coverage

License

Who Can Use It

• Data scientists: For exploratory data analysis and building predictive models related to music.
• Music researchers and academics: Investigating the characteristics of unpopular music and contributing to music information retrieval studies.
• Developers: Creating applications or tools that leverage audio features for music categorisation or discovery.
• Anyone interested in music data: Exploring the technical aspects of music production and listener engagement.

Dataset Name Suggestions

• Spotify Unpopular Songs Audio Features
• Unpopular Spotify Tracks Dataset
• Spotify Low Popularity Music Data
• Forgotten Spotify Melodies: Audio Characteristics

Attributes