Code Monkey home page Code Monkey logo

music-data's Introduction

Music Data

Click HERE to access the app!

How the app works:

This is an interactive visual display of music data pulled from Kaggle.com!

When you open to the app you have a couple of options and you can select which graph you want to look at by clicking through the tabs at the top of the app!

  • How do the most popular songs show popularity?
    • The first bar chart will allow you to visualize the top 9 most popular songs, and filter through different factors that will allow the user to visualize how the most popular songs exemplify certain characteristics such as Danceability, Tempo, Acousticness, and more!
  • How do all the songs in the dataset exemplify certain characteristics?
    • The second plot will allow the user to choose different song characteristics and plot them against each other to visualize a distribution.
  • Are certain artists more popular than others?
    • The third pie chart allows you to visualize in a pie chart an input amount of the users choice anywhere from 0-100 songs, and will display fraction(s) of those songs by mood
  • What songs can I dance to?
    • The final table allows the user to select how many songs they want to display and then showing that number of the top songs by danceability!

The Columns:

  • Song ID:
    • The Spotify URL of a song.
  • Name:
    • The name of a song.
  • Artist(s):
    • The artist(s) in a song.
  • Danceability:
    • How suitable a track is for dancing based on a combination of musical elements.
  • Energy:
    • A measure (from 0-1) that describes a measure of intensity and activity.
  • Key:
    • What key the song is in (by number) with 0 being C, 1 is C#, 3 is D and so on.
  • Loudness:
    • How 'loud' a song is (measured in decibels)
  • Mode:
    • Indicates the modality (major or minor and represented in 0s and 1s respectively) of a track.
  • Speechiness:
    • Detects how often there are words in the track (0-1 with .66ish being absolutely all speech)
  • Acousticness:
    • How likely it is that the track is fully acoustic with 1.0 being very confident that the track is acoustic.
  • Instrumentaless:
    • Can predict how likely it is that a track contains vocals or not. In this context certain vocal noises like "ooh, ahh" are treated as instrumental sounds.
  • Liveness:
    • Can detect the presence of a live audience in the background of a song track. a value of .8+ is a strong likelihood that a track is live.
  • Valence:
    • A measure of the 'musical positveness' conveyed by a track. a higher valence closer to 1 indicates that a song is more positive i.e. 'happy' or 'cheerful'
  • Tempo:
    • The overall estimated tempo of a track in BPM, this is the pace or speed of a song.
  • Duration:
    • How many milliseconds long the track is. duration_msThe duration of the track in milliseconds.
  • Time Signature:
    • The time signature ex. 4/4 or 3/4 of the song.

music-data's People

Contributors

annep7-1760336 avatar stian-eng avatar marwa10 avatar michaelzhz avatar

music-data's Issues

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.