Series Overview
This series applies data science techniques to understand the U.S. Congress through legislative data. From web scraping congressional records to building machine learning models for policy classification, we explore how data can illuminate the patterns and priorities of American democracy.
What You’ll Learn
- Web scraping techniques: Extracting legislative data from Congress.gov responsibly and systematically
- Exploratory data analysis: Uncovering patterns in bill introductions, committee activity, and voting behavior
- Text classification: Using machine learning to automatically categorize legislative content
- Political data visualization: Creating clear, informative charts that reveal legislative trends
- Data ethics: Respecting rate limits and building reproducible research workflows
The Journey
Exploring the 117th U.S. Congress establishes the data foundation through comprehensive web scraping and exploratory analysis. Learn how bills move through the legislative process, which parties introduce what types of legislation, and how success rates vary across policy areas.
Congressional Bill Policy Area Classification applies machine learning to automatically categorize bills by policy area. Using 48,000+ bills from three Congresses, we build baseline models that can distinguish between healthcare, defense, economics, and other policy domains.
Technical Skills
This series demonstrates practical data science workflows:
- Web scraping: BeautifulSoup, Selenium, and respectful crawling practices
- Data processing: Cleaning and structuring legislative records for analysis
- Visualization: Interactive charts with Plotly for exploring political patterns
- Text processing: TF-IDF vectorization and feature engineering for political text
- Machine learning: Baseline models (Naive Bayes, Logistic Regression, XGBoost) for classification
Real-World Applications
These techniques enable broader applications in:
- Civic technology: Building tools that help citizens understand legislation
- Political research: Quantitative analysis of legislative behavior and priorities
- Journalism: Data-driven reporting on congressional activity and trends
- Government transparency: Making legislative processes more accessible through data
Broader Context
Understanding congressional data provides insights into:
- How democratic institutions function in practice
- The relationship between political rhetoric and actual legislative activity
- Temporal patterns in policy priorities and partisan behavior
- The mechanics of how bills become laws (or don’t)
Future Directions
This foundation enables more advanced analyses:
- Predictive modeling of bill success based on text content and metadata
- Network analysis of co-sponsorship patterns and political alliances
- Geographic analysis linking legislation to district characteristics
- Sentiment analysis of legislative language over time
Perfect for data scientists interested in civic applications, political researchers seeking quantitative methods, or anyone curious about applying machine learning to understand democratic processes.