Data Mining and Knowledge Discovery on KKBOX Music Data
KKBOX is a leading music streaming service in Asia, boasting the world's most comprehensive collection of Asian pop music. This paper conducts a data mining analysis on the music dataset that KKBOX has provided to the Kaggle community. Through initial cleaning and analysis of the dataset, we propose four data mining–related questions: correlation analysis among features in the dataset, song clustering, user clustering, and predicting whether a user will listen to a particular song repeatedly within a month. We then select appropriate algorithms—such as the K-prototypes clustering algorithm, the t-SNE high-dimensional data visualization algorithm, and the LightGBM algorithm—to address these data mining problems, and finally present corresponding conclusions. In doing so, we explore the application of data mining algorithms to a real-world dataset, transforming knowledge from the data mining classroom into truly practical technology.