Predicting Music Popularity Using Machine Learning Algorithm and Music Metrics Available in Spotify
Neha Sharma, Prashant Pareek, Mr. Pushpak Pathak, Ms. Nidhi Sakariya
Abstract
The exponential growth of online music streaming has given birth to many new platforms among which, the widely used platform is Spotify. The most popular music streaming app's data can be used to predict the capability of a song to be popular before its release with the help of attributes like loudness, energy, acousticness, etc. which is defined when the song is being made. This study helps to predict the popularity of the song using the song metrics available in Spotify by applying Random Forest classifier, K-Nearest neighbour classifier and Linear Support Vector classifier to compare which of these models can effectively predict the popularity. The results found that Random Forest works the best for predicting popularity with high accuracy, precision, recall and F1-score.