Skip to content

Using Spotify API to create Beatles data set and employ machine learning techniques

Notifications You must be signed in to change notification settings

hcstahl/TheBeatlesMachineLearning

Repository files navigation

TheBeatlesMachineLearning

Using Spotify API to create a data set which include The Beatles discrography. Followed this guide to create the data set

Ideas for the project

  • Classify songs with their correct album
  • Predict whether a song is first/last track based on song characteristics

Data Set Features

  • 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.
  • energy: a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity.
  • key :The key the track is in
  • 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.
  • 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 :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.
  • 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.
  • instrumentalness: Predicts whether a track contains no vocals. "Ooh" and "aah" sounds are treated as instrumental in this context.
  • 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.
  • valence :A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track.Tracks with high valence sound more positive.
  • tempo : The overall estimated tempo of a track in beats per minute (BPM).
  • time_signature: An estimated time signature. The time signature (meter) is a notational convention to specify how many beats are in each bar.
  • track_name: Track name without any extra information
  • track_number: Track number on the album
  • short_album_name: Album name without any extra information ex: Remastered,year etc
  • release_date: Year/Month/Day
  • album_cover : URL to the album image
  • duration_seconds : How long the song is in seconds
  • track1 : If the song is the first track on the album 1 = yes
  • last_track: If the song is the last track on the album 1 = yes

About

Using Spotify API to create Beatles data set and employ machine learning techniques

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published