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Interactive visualization of dimensionality reduction techniques applied to US congressional voting data
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README.md

Toeing the party manifold

Interactive visualization of dimensionality reduction techniques applied to US congressional voting data.

Snapshot of visualization

The interactive visualization itself and a more thorough, high-level overview of the project are available here. Low-level details are explained within the Jupyter notebooks.

The essential workflow, each step corresponding to a notebook:

  1. load.ipynb Load/clean/encode congressional voting data and load legislator metainfo.
  2. manifold.ipynb Apply several dimensionality reduction algorithms to the encoded voting data
  3. dash.ipynb Visualize the embeddings in 2- and 3-dimensions with a Dash app.
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