Sounds like a great project. Let’s break it down into three steps: collect clean data, apply a clustering or correlation model, then create a visual dashboard that tells the story in a few key metrics. What data sources are you looking at?

Nice set of sources—just make sure your tables are normalized so foreign keys line up. For clustering, start with a dimensionality‑reduction step: PCA or t‑SNE on the weighted lobbying amounts per sector. Then run K‑means or hierarchical clustering, evaluate with silhouette scores, and map clusters back to policy areas. Finally, build a Tableau or Power BI dashboard that filters by state, year, and industry, so the story is instantly actionable. What’s the biggest data quality hurdle you’re facing right now?

You’ll need a normalization routine before you can even think about clustering. Start by creating a master schema: currency, date, client, industry, line‑item category, amount. Write a script that converts every currency to a base currency using a historical rate table, then splits bundled totals by proportioning them across categories—use historical averages or a machine‑learning imputer if you have enough data. Store the cleaned rows in a separate table so the raw data stays untouched. Once every record is in the same units and format, your PCA will actually reflect real patterns instead of noise. What tooling are you using for the transformation step?

Looks solid. Just double‑check the ECBE daily rates for dates that fall on weekends or holidays; fallback to the previous business day’s rate to avoid nulls. For the SQLite lookup, keep it in-memory during ETL runs to reduce I/O, but cache it in Redis if the lookup table grows. Remember to log every conversion step with a checksum so you can audit the normalization later. Once the pipeline is running, set up a cron job that recomputes the historical averages quarterly; that will keep your imputation model up‑to‑date without manual intervention. What’s your next milestone after normalization?

Nice roadmap. Just keep the outlier flag binary so you can later switch to a z‑score if needed; it’ll save recomputing. When you compare silhouette scores, remember K‑means can be sensitive to initial centroids—run it at least 10 times and pick the best. For the hierarchical run, try Ward’s linkage; it usually gives tighter clusters. Once you map clusters to policy areas, create a quick drill‑down chart—users love to see “Cluster 3: 70 % tech lobbying, 15 % defense.” That’ll make the dashboard actionable. Good luck, and keep the Git commits descriptive—you’ll thank yourself later.