Machine learning has now permeated multiple disciplines, even politics. The current landscape in the US is rife with data scientists and other quantitative experts making predictions about ongoing and upcoming elections. Considering the Congressional Voting Records dataset from the UCI machine learning repository - (https://archive.ics.uci.edu/ml/datasets/Congressional+Voting+Records).
The dataset contains two files: one with a “.names” suffix and one with a “.data” suffix. The actual data is in the “.data” suffix and “.names” describes the metadata (i.e., describes what the different columns mean). Note that each row of the “.data” file contains one instance and includes both features and the class label.
The machine learning problem here is to take the votes of US congressmen/congresswomen as input and predict whether they are a Republican or a Democrat.