I would vote for idea #2 since I remember seeing the gun ownership data being very detailed. I think you need to pin down the model you will use. Clustering makes sense, just to get a sense at if there are neighbors that are similar based on crime and gun ownership, but I think you should do a lat/long type analysis where you'd be checking if high crime polygon areas have more gun owners living in them, for safety or to commit a crime. Presumably the first because criminals may not want to be registered in the gun ownership database in case they get tracked. You can make really cool maps.

• Strengths:

  • Solid methodological choices.
  • Good use of maps and. bar plots—both make sense.
  • Your map visualization was excellent—looked exactly as I was imagining.

• Areas for Improvement:

  • Normalization: Consider arrests per person or gun ownership per person instead of raw counts.
  • I liked how you highlighted specific neighborhoods—you could check correlations across them.
  • Try repeating the analysis with just gun ownership data—does it change insights?
  • Interesting ethical implications—consider whether there is a correlation between:
    • Gun offenses
    • Gun ownership
    • Public arrests
  • You might find this TED talk relevant to your topic: https://www.ted.com/talks/steven_levitt_the_freakonomics_of_crack_dealing?language=en

• Nice background intro slide!
• Great purpose slide too. Maybe mention something along the lines of "these are the important implications for stakeholders" at the end of that slide.
• Thanks for the mention 😄
• Nice convert screenshots.
• I think your discussion of data processing steps were extremely helpful to others because they get a chance to hear about the interesting situation you have with millions of rows from multiple datasets and hearing how you are overcoming these challenges is really cool.
• Your potential models make sense too. Maybe consider inference too using linear regression.

https://github.com/Rahmasemma/Data-400-final-project

• Great detailed explanation of your data. Maybe spend more time on how FDA maintains this database, how do they get their data etc.
• I think you can make the count plots presented as tables. There could be some more creative ways to visualize counts, maybe see r/dataisbeautiful for inspiration.
• Correlation/causation is huge in your project. You should mention that what you have is correlation only. That is, the top side effects drug you have does not necessarily be causing death. You explained this later in your talk but you should probably move it to earlier.
• Maybe a way to find "really bad" drugs that may really be causing adverse conditions on healthy people, you can try gathering information on drugs and the population they are used by. If there are drugs most often taken by older people you can trim those away and only focus on those taken by say general population.
• I agree your box plot graph is awesome.
• One possible focus you can go for you can scan court cases that judges found guilty or not guilty and check the numbers for those drugs.
• You should expand more on implications because arguably your project has the biggest implications out of all talks today since it's about health. Similarly for ethics, can we get any implications from the FDA data showing bias?