https://github.com/radoslawregula/geo-music-classification

Jupyter notebook implementing a classification solution to the geographical origins of music problem.
https://github.com/radoslawregula/geo-music-classification

classification jupyter-notebook machine-learning pandas python random-forest-classifier scikit-learn

Last synced: 4 days ago
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Jupyter notebook implementing a classification solution to the geographical origins of music problem.

• Host: GitHub
• URL: https://github.com/radoslawregula/geo-music-classification
• Owner: radoslawregula
• Created: 2020-02-09T18:05:38.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-09T19:04:42.000Z (almost 5 years ago)
• Last Synced: 2024-11-20T07:18:32.030Z (2 months ago)
• Topics: classification, jupyter-notebook, machine-learning, pandas, python, random-forest-classifier, scikit-learn
• Language: Jupyter Notebook
• Size: 34.2 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Classification of music's country of origin based on audio features

The notebook offers a way to solve a problem stated in Fang Zhou, Claire Q and Ross. D. King, Predicting the Geographical Origin of Music, ICDM, 2014.

The solution is based on classification using random forest classifier. To measure the accuracy of predictions, the custom geographical accuracy metric was designed.

The metric is described in the notebook and its coefficients can be found in the attached .txt file.

The method uses scikit-learn library's implementations of machine learning methods and pandas' tools for handling data.

The data set was created by Fang Zhou, The University of Nottinghan, Ningbo, China and was shared [here](http://archive.ics.uci.edu/ml/datasets/Geographical+Original+of+Music#).