Song year prediction using Apache Spark
Prakhar Mishra, Ratika Garg, Akshat Kumar, Arpan Gupta, Praveen Kumar · 2016
In this paper, we aim to predict the year in which a particular song was officially released. Listeners often have particular affection for music from certain periods of their lives (such as high school), thus, the predicted release year of a song could be a useful basis for recommendation. Furthermore, a successful model of the variation in music characteristics, through the years, could throw light on the long-term evolution of popular music. In our study, different machine learning algorithms available in the Apache Spark Machine Learning library (MLlib) are applied on a sample of Million Song Dataset (MSD). Different learning algorithms were applied for training and prediction purpose. Also, the training times are compared for single and multinode cluster environment using Apache Spark.