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A machine learning (regression) web application that predicts the song popularity using Spotify audio features dataset.

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Regrify

Play around with the model and make predictions using the web app I made.

regrify

Description

This tool predicts Spotify popularity on a scale of 0 to 100. The popularity of a track is algorithmically calculated, and is a combination of how many plays a track has and how recent those plays are.

The model was trained on Spotify audio features dataset, using an Elastic Net regression model. It has an RMSE of 11.28. This project was made over a 1-week span in August 2022.

Feature Glossary

  • Genre: A category that identifies some pieces of music as belonging to a shared tradition or set of conventions.
  • Duration: This is the length of the song in seconds.
  • Tempo: This is the estimated speed of the song in beats per minute.
  • Loudness: The overall loudness of a song in dB on a scale of -20 to 0.
  • Release Year: A year on which a track is due to become available for the public to listen.
  • Explicit: Indicates whether a track has curse words or language or art that is sexual, violent, or offensive in nature.
  • Danceability: Measure of how danceable a song is from a scale of 0 to 1.
  • Energy: A perceptual measure of intensity and activity from a scale of 0 to 1.
  • Speechiness: Detects the presence of spoken words in a song on a scale of 0 to 1. A lower number (0-0.33) tends to indicate less speech, a moderate number (0.33-0.66) tends to indicate a mix of music and spoken word like rap, and a higher number (0.66-1) tends to indicate something like a poetry album.
  • Acousticness: A confidence measure on if the song is acoustic on a scale of 0 to 1.
  • Instrumentalness: A measure on a scale of 0 to 1 on whether a track has no vocals (excluding adlibs like ooh-ahh's). Values about 0.5 tend to indicate an instrumental track.
  • Liveness: Detects the presence of an audience in a recording on a scale of 0 to 1. A value of 0.8 or above indicates a strong chance that the song is a live recording.
  • Valence: A measure from 0 to 1 indicating the "musical positiveness" of a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

Issues

If any issues are found, they can be reported here.

License

This project is licensed under the MIT license.

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A machine learning (regression) web application that predicts the song popularity using Spotify audio features dataset.

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