Top Tracks Playspace

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

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.