Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: The purpose of this project is to construct a prediction model using various machine learning algorithms and to document the end-to-end steps using a template. The Song Year Prediction dataset is a classic regression situation where we are trying to predict the value of a continuous variable.
INTRODUCTION: This data is a subset of the Million Song Dataset, http://labrosa.ee.columbia.edu/millionsong/, a collaboration between LabROSA (Columbia University) and The Echo Nest. The purpose of this exercise is to predict the release year of a song from audio features. Songs are mostly western, commercial tracks ranging from 1922 to 2011, with a peak in the year 2000s. The data preparer recommended the train/test split of the first 463,715 examples for training and the last 51,630 examples for testing. This approach avoids the ‘producer effect’ by making sure no song from a given artist ends up in both the train and test set.
In this Take1 iteration, we will construct several Multilayer Perceptron (MLP) models with one hidden layer of 16, 32, 64, and 128 nodes. The single-layer MLP model will serve as the baseline model as we build more complex MLP models in future iterations.
ANALYSIS: In this Take1 iteration, all models processed the test dataset and produced an RMSE around 9.50. However, the single-layer models do not exhibit a stable curve when making predictions with the test dataset.
CONCLUSION: For this iteration, the different model architectures produced similar RMSE. For this dataset, we should consider experimenting with more MLP models that have more hidden layers.
Dataset Used: YearPredictionMSD Dataset
Dataset ML Model: Regression with numerical attributes
Dataset Reference: https://archive.ics.uci.edu/ml/datasets/YearPredictionMSD
Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. The Million Song Dataset. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), 2011.
One potential source of performance benchmarks: https://www.kaggle.com/uciml/msd-audio-features/home
The HTML formatted report can be found here on GitHub.