ML model to predict bill-to-committee referrals based on historical patterns, bill text, sponsor, and subject.
Problem
Understanding bill referral patterns is important for legislative strategy, but manually analyzing historical data to predict which committees will receive jurisdiction over a new bill is time-consuming.
Solution
Develop a predictive model, using machine learning techniques, to forecast bill referrals to committees based on historical referral patterns. The model should consider factors such as bill text, sponsor, and subject matter. The output could be:
- A standalone dataset of predicted referrals.
- An add-on application that integrates with existing legislative tracking platforms like Congress.gov or GovTrack.us.