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Classify Song Genres from Audio Data

Rock or rap? Apply machine learning methods in Python to classify songs into genres.

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12 Tasks1,500 XP14,072 Learners

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Project Description

Using a dataset comprised of songs of two music genres (Hip-Hop and Rock), you will train a classifier to distinguish between the two genres based only on track information derived from Echonest (now part of Spotify). You will first make use of pandas and seaborn packages in Python for subsetting the data, aggregating information, and creating plots when exploring the data for obvious trends or factors you should be aware of when doing machine learning.

Next, you will use the scikit-learn package to predict whether you can correctly classify a song's genre based on features such as danceability, energy, acousticness, tempo, etc. You will go over implementations of common algorithms such as PCA, logistic regression, decision trees, and so forth.

Project Tasks

  1. 1
    Preparing our dataset
  2. 2
    Pairwise relationships between continuous variables
  3. 3
    Splitting our data
  4. 4
    Normalizing the feature data
  5. 5
    Principal Component Analysis on our scaled data
  6. 6
    Further visualization of PCA
  7. 7
    Projecting on to our features
  8. 8
    Train a decision tree to classify genre
  9. 9
    Compare our decision tree to a logistic regression
  10. 10
    Balance our data for greater performance
  11. 11
    Does balancing our dataset improve model bias?
  12. 12
    Using cross-validation to evaluate our models

Technologies

Python Python

Topics

Data ManipulationData VisualizationMachine Learning
Lina Tran HeadshotLina Tran

PhD Candidate at University of Toronto

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