"Predict the popular year for a song."

In this project, we will attempt to discover the features behind the popular music of each generation. For instance, if Britney Spears, “Oops I Did It Again” made the charts in 2001, and The Beatles’ “Real Love” made the charts in 1996, we want to see what made the music popular back then – was it the timbre, audio quality, or lyrics? We will then attempt to build a model that is able to predict when the song would have been most popular, which can be useful for future music analyses.

1. Obtain the Million Song Dataset, or Million Song Subset: http://labrosa.ee.columbia.edu/millionsong/pages/getting-dataset
2. Obtain the sample codebase. https://github.com/tb2332/MSongsDB
3. Download SQLite DB Browser: http://sqlitebrowser.org
4. Download Panoply (to open HDFS5): http://www.giss.nasa.gov/tools/panoply/download_mac.html
5. Clone the repo, and work in dataexploration.py. Set your own path to the Million Song Dataset subset in setup.py.

Either:

• Use setuptools: easy_install -U pyechonest
• Download the zip from the releases page: https://github.com/echonest/pyechonest/releases
  • Unpack, navigate to the echonest directory and run: sudo python setup.py install

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.