Project Summary: I combined several datasets to answer the question - where should someone live in Chicago if they want to find a cheap apartment near a lot of bars?
- I scraped Craigslist ads for apartments and filtered them based on location, cost, and proximity to public transit.
- I used the Foursquare API to get information about nearby businesses (specifically bars).
- Then I combined the information on a map and analyzed the results filtered by train stop.
Python, pandas, numpy, matplotlib, Nominatim/OpenStreetMap, BeautifulSoup for web scraping, folium maps, FourSquare API for business info.
It was a fun project and while the rental market is constantly changing I think the information is still valuable.
Feel free to check it out!
AppliedDataScienceCapstoneReportV2.pdf is my full report
CheapChicagoLiving.ipynb is my Jupyter notebook
CheapChicagoLivingPresentation.pdf is my final presentation slides