This project shows all the AI-related legislation proposed in Congress till date.
The data is merged with the Lobbying Disclosure API to show which tech companies lobbied for which corresponding bills.

Goals:
Merge two different datasets, do data analysis with my new-formed dataset.

Results:
The project showed which states had the most AI-related bills, which party (Democrat/Republican) proposed the most bills, and also had a breakdown of which representative had the most bills in Congress.
Finally, one part of the analysis includes a chart of which tech companies have lobbied for the most bills.

Skills learnt:

• Merging different datasets and creating one dataframe.
• Working with complicated APIs (lots of data and every command only generated about 25 results—a cap set by the website).
• Using a combination of Playwright and BeautifulSoup.

Shortfalls/Challenges:
The Lobbying Disclosure API is very inconsistent, so a part of the analysis (tech companies and the bills they lobbied for) is an undercount.
I could not find all the bills a company lobbied for because of how inconsistently every party (companies, clients, lobbyists) filled out the Lobbying Disclosure form.

What I would do better:
Look for a better data source and learn to use the API in a way where I can incorporate these inconsistencies.