Skip to content

reccleston/music-project

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Top Tracks Playspace

Team Members: Sarah D. Hood, Jini Hassan, Ryan Eccleston-Murdock, Wasif Khan, and Angeli Lucila

Songs

Dataset we used:

  • Top Spotify Songs from 2010-2019 - Kaggle
    • Context: The top songs BY YEAR in the world by spotify. This dataset has several variables about the songs and is based on Billboard
    • Content: There are the most popular songs in the world by year and 13 variables to be explored. Data was extracted from: http://organizeyourmusic.playlistmachinery.com/
    • Variables Measured:
      • Popularity/pop: how popular a song is. The higher the number, the more popular it is.
      • Speechiness/spch: how much spoken word is in the track.
      • Acoustic-ness/acous: how acoustic the song is.
      • Duration/dur: how long the track is (in seconds).
      • Valence/val: how positive the track is.
      • Liveness/live: how likely it is for the track to be a live recording.
      • Decibels/dB: how loud the track is.
      • Danceability/dnce: how easy it is to dance to the song.
      • Energy/nrgy: how energetic the song is.
      • Beats Per Minute/bpm: how many beats per minute, or, the track’s tempo.

Summary and Motivation:

This webpage displays information from a Kaggle dataset on the top songs (according to Billboard rankings) worldwide on Spotify between 2010 and 2019.

We were inspired to work on this dataset because Spotify is a widely used streaming application, and drawing connections between different song qualities (here called "factors") seemed like an intriguing idea.

Folders Directory

  • Wasif Folder includes all relevant files for our website. Use this directory to view.
    • App.py contains the flask app that you will launch to view our website.
    • static folder contains all the javascript/css files for the visualizations
    • schema folder contains the schema for the database.
  • Angeli folder shows relevant files for the bar chart visualization.
  • Jini folder shows relevant files for the sunburst visualization.
  • Ryan folder shows relevant files for the correlation heatmap visualization.
  • Sarah folder shows relevant files for the bubble chart visualization.

How To Create The Database On Your Computer

  1. Open pgAdmin and create a database called "music_project".
  2. Use the schema file to create the tables.
  3. Import data_cleaned.csv to the "data_cleaned" table.
  4. Import corr_heatmap_vals.csv to the "corr_heatmap_vals" table.
  5. Import year.csv to the "year_table" table.
  6. Ensure that you modify your pgAdmin credentials in the variable "connection_string" in the app.py file.

Key Takeaways

  • Correlation Heatmap: The three strongest positive correlations found are between decibels and energy (0.538), valence and danceability (0.502), and valence and energy (0.41). The strongest negative correlation was between acousticness and energy (-0.562).
  • Sunburst: The pop genre is always 'popular', making up 80% of the top tracks.
  • Bubble Chart: 2013 marks the year where a greater variety of genres start appearing within the top tracks.
  • Bar Chart: 2013 and 2015 tracks are high in positivity (valence), danceability and energy. They also seem to be years with the most popular tracks. Meanwhile 2012 and 2019 mark a downturn in all these factors.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published