NOTE: Please create a file named "api_key.txt" in the project root folder containing your Prorepublica Congress API Key as a line before running the notebook
Predicting how Congressional legislators vote is important for understanding their past and future behavior. In this project, we explore U.S. senators voting patterns using a network-based approach.
- Can we predict the political stances of individuals as well as which senators are the closest to their ideology ?
- Can a specific subset of senators, called the swing votes, be used to accurately predict the outcome of a vote ?
- Main: contains a step by step demonstration of the main results shown in the report.
- Feature-Engineering: contains all the code used to process the raw data into a format that is convenient to work with
- Data-Acquisition: contains all the code used to retrieve the dataset from ProPublica's congress API
Our project required some boilerplate code, especially for graph visualization that we chose to factorize in helper modules. Their contents are briefly summarized below.
- cluster_utils.py Helper functions for running the KMean algorithm.
- plot_utils.py All the boilerplate code required for creating the graph and the embedding vizualizations.
- request_utils.py Code fetching bill and roll call vote information from the relevant U.S governmental websites.
- transductive_learning_utils.py Code for running the variational minimization problem.
- utils.py Remaining utility functions that didn't fall into one of the previous categories