Finish SBA Module

[jegeronimo]

Due: 6/20

Changes to current work:

• Abstract code in a utils.py file
• 4. Create widget so students can see the SBA-Approved Loan amounts in sorted chunks of 10 (instead of fixed top 15); Or create a similar map to 7. instead since that makes more intuitive sense
• 5. Scaffolding, consider if it fits in general notebook narrative
• 6. Scaffolding, make similar widget by chunks of NAICS codes. Migrate code to plotly. Maybe group by first two digits of NAICS code given the other 4 digits don't matter (I asumme just more specific subset of original sector, do research)
• 7. Either scratch this or look more specifically to California

To be added:

• Explore Los Angeles more specifically
• Look into what loans were approved vs. not (compare distributions, features, etc.)
• Incorporate ethics and representation component (may need to search for another dataset)

[jegeronimo]

• 6. Look at the contrast between all of the US vs CA
• 8. Double check code
• Look at the criteria for getting a loan (certain reasons for loans to be approved in certain places/demographics/industries/etc.)
• Incorporate 2022 Small Business Profile into its own section for data representation component