Popularity Prediction of Music by Machine Learning Models
DOI:
https://doi.org/10.54097/hset.v47i.8162Keywords:
Music, xgboost, XGBRegressor, data.Abstract
Each piece of music has a considerable amount of information and attributes, and each listener has their own unique taste in music, with some songs being very popular and others being relatively niche. It becomes worthwhile to examine what kinds of music are more popular. It’s clear that in this subject, the analysis and study of the available data is a "crucial first step". In this paper, we use several fitting models in machine learning as the theoretical basis, using pandas, sklearn, xgboost, and other related tools in python, to predict the popularity of music based on a dataset of music information originating from Kaggle The most suitable machine learning model is founded for predicting music popularity and the effectiveness of its fit are evaluated. This study provides a methodological basis for finding the factors influencing music popularity in post-order studies and can be a key study in determining the factors that influence the popularity of music in the future.
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