Framework of Voting Prediction of Parliament Members

Abstract Tracking lawmakers’ voting behavior is vital for government transparency. Although many parliaments publish voting records online, these datasets are often complex and difficult to analyze. This research presents the Voting Prediction Framework (VPF), a machine learning-based system designed to predict parliamentary voting outcomes at both the individual legislator and bill levels across multiple countries. VPF aims to support legislative transparency, streamline parliamentary work, and aid in policy refinement by identifying bills with a low likelihood of passing. The framework comprises three main components: (1) Data Collection – retrieving voting records from various countries using APIs, web crawlers, and structured databases; (2) Parsing and Feature Integration – enriching data with relevant features such as legislator seniority and bill content; and (3) Prediction Models – applying machine learning algorithms to forecast individual votes and bill-level outcomes. VPF is released as open source to encourage accessibility and adaptation. To validate VPF, we analyzed over 5 million votes from five countries: Canada, Israel, Tunisia, the United Kingdom, and the United States. Results indicate up to 85% precision in predicting individual votes and up to 84% accuracy for bill outcomes, demonstrating VPF’s effectiveness as a tool for political analysis and public insight into legislative processes.