mini project idea

[mcao694]

No description provided.

[ernbilen]

This is extremely interesting. I'm curious what determines rat presence the most. Is it the old buildings, temperature, presence of restaurants nearby? Lenient restaurant inspections, simply population density in a neighboorhood or just overall dirtiness of the neighborhood? Really fascinating. I am curious about what you will find.

[DerekChibbaro]

This is a great idea to help the farm expand its distribution to students as well as potentially expanding into other markets. It’s great that the college farm keeps track of many data categories. A linear regression model may be useful in predicting how significant certain categories impact sales and overall inventory values.

[ernbilen]

• I liked your data which goes back to 2000s.
• Who buys the pesticide? If direct data is unavailable, try correlating with farms per ZIP code (I liked that you mentioned something similar).
• Tying your discussion with rodents, I think focusing on specific areas like NYC could be interesting. Does pesticide purchases correlate with w/ bed bugs, rodents data?
• Consider whether pesticide use correlates with restaurant scores or health data.
• Great ethics discussion—keep that focus!

[ernbilen]

• I like the topic a lot personally. I think you can show a hook slide, maybe showing the price of an item or a whole basket at a retailer vs another one. You could show screenshots.
• Scraping Walmart can be very difficult because of cloudflare. Let me know how it goes.
• It's nice that you showed your desired dataset visually. It makes it so much easier to understand.
• I think you need to scrape a lot of items to detect price differences. Categories are really interesting, like you said, groceries but also electronics etc.
• You should probably use the same zip code. Pick one zip code and get data from multiple stores is the way to go.
• As a related question, I wonder if target is consistently more expensive than Walmart because their store is nicer etc.

[ernbilen]

• Great intro slide!
• I'd make the motivation slide shorter, a la your intro slide. Less words, more easy-to-remember concepts is the way to go.
• Research questions slide is great as well.
• You achieved a monumental work by scraping ~1000 items!!
• Olivia's team in the other section also scraped grocery store price data. You should have a chat!
• I personally find stacked bar charts hard to read. Try another viz for these, you can come up with something creative for sure. See r/dataisbeautiful for inspiration.
• Should definitely work on your feature importance graph. It's hard to read. Try making it simpler.
• How do you explain differences in R^2 across grocery stores?
• I think inference makes more sense than prediction on your case. That is, checking the correlation between prices across stores.
• Also I got lost in data a bit. What are 2-3 simple takeaways from your project?
• For implications I suggest instead of saying eg our project matters to consumers, you should explain it as in, it matters because we find such and such. I hope that makes sense.