Streaming Activity Dataset

4 years of diverse music streaming data across platforms and ages

By Sean Miller [source]

About this dataset

The dataset consists of two main files: Scrobble_Features.csv and My Streaming Activity.csv. The Scrobble_Features.csv file contains detailed information about the music tracks, including genre, duration, popularity, and various audio features. On the other hand, the My Streaming Activity.csv file offers 4 years' worth of music streaming data from multiple platforms.

Key columns in these files include: - Performer: The name of the performer or artist. - Song: The title of the song. - Album: The name of the album that each song belongs to. - spotify_genre: The genre(s) assigned to each song according to Spotify's classification. - spotify_track_preview_url: URLs providing previews for each song on Spotify. - spotify_track_duration_ms: The duration of each song in milliseconds. - spotify_track_popularity: A popularity score indicating how popular each track is on Spotify. - spotify_track_explicit: A boolean value indicating whether or not a track contains explicit content.

Further musical attributes are also included: - danceability: A measure determining how suitable a song is for dancing based on various musical elements. - energy: An indicator measuring the intensity and activity level present in a song's composition. - key: Identifies the key signature (e.g., C major) that each track is performed in - loudness: Reveals how loud or soft a given track is overall in decibels (dB). - mode : Indicates whether a given track is composed in major or minor scale/mode. These attributes aim to provide insights into different aspects of a song's overall composition and impact.

Additionally, this dataset offers information about the timestamps when streaming activities occurred in both Central Time Zone (TimeStamp_Central) and Coordinated Universal Time (UTC) (TimeStamp_UTC).

How to use the dataset

In this guide, we will walk you through how to effectively use this dataset for your analysis or projects. Let's get started!

Understanding the Columns

Before diving into analyzing the data, let's understand the meaning of each column in the dataset:

• Performer: The name of the performer or artist of the song.
• Song: The title of the song.
• spotify_genre: The genre(s) of the song according to Spotify.
• spotify_track_preview_url: The URL of a preview of the song on Spotify.
• spotify_track_duration_ms: The duration of the song in milliseconds.
• spotify_track_popularity: The popularity score of the song on Spotify. (Numeric/Integer)
• spotify_track_explicit: Indicates whether the song contains explicit content. (Boolean)
• danceability: A measure of how suitable a song is for dancing based on a combination of musical elements. (Numeric/Float)
• energy: A measure o fthe intensity and activity level present in a track.(Alternatively it can also represent acoustic as well). (Numeric/Float)
  • 'key'- represents grouping.of songs based on keys found within that specific set pf songs
  • 'loundess' represents how loud or.silent that particular tract is usually defines by Clown Circle Diameter'.(diameter varies with loudness(sound pressure level). -'mode':defines what type/modeis represented(i.e If Major mode denoted by '1',If minor mood is denoted.by value '0') -'Speechiness':Detecting spoken words(actually presence/removal of spoken dialects.song verses). -Acousticness:Probability of track being acoustic,concerted,edt. -instrumentalness-instrumental.also calcylates effectively considering odds and ends ( for example; Intensity of beat.Solo drumming. -'liveness':a sentiment reflecting the probability that a song was performed since the recording being analysed 'valence'-The musical positivity/cheerfulness conveyed by a track.'1'represents most positive ;'0'mostly one(most presumably sad) -tempo:'Rate at which particular beats re occur in.oncluding beats); BPM (

Research Ideas

• Music Recommendation System: This dataset can be used to develop a music recommendation system by analyzing the streaming activity and audio features of different songs. By understanding the preferences and listening habits of users, personalized music recommendations can be generated for individuals or households.
• Genre Analysis and Trends: The dataset provides information about the performer, genre, and popularity of songs. This data can be utilized to analyze trends in music genres over the years, identify popular artists in different genres, and understand the ...

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