Linear Regression application to predict the popularity index in Spotify

• Autores: Vasquez Alvarez, Cesar, Coaquira Cuevas, Edith, Mendoza Hilasaca, Emerson, Pinto Ñaupa, Jeffrey
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/110
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/110; https://doi.org/10.48168/innosoft.s12.a110; https://purl.org/42411/s12/a110; https://n2t.net/ark:/42411/s12/a110
• Nivel de acceso: acceso abierto
• Materia: Python; Linear Regression; Predict; Regresión Lineal; Predicción

Currently, streaming music services have become one of the main means of music consumption around the world. Spotify offers music streaming services and covers more than thirty million songs. Every year there is an increase in the production of songs so it is more difficult for a song to establish itself as a hit in the market. The objective of this work was to apply the Linear Regression modeling technique to find a trend of the data set on the popularity index of songs on the Spotify platform, in this way predict a result with new data that enters. A quantitative methodology was applied based on measurable data that were taken as datasets. As a result, a mean square error of 94.79 and a variance of 0.20 were obtained. The conclusion of the work is that the dataset used was not the ideal according to our objective.