Final Project for 2221 CMPINF 0010 SEC1010 BIG IDEAS IN COMPUTING & INFORMATION
- Shinwoo Kim — SHK148@pitt.edu
- Kavin Sankar — KAS595@pitt.edu
- Jonathan Ryu JHR25@pitt.edu
This github repository is about finding the best neighborhood in Pittsburgh using Python's data analysis framework - pandas. Our team used python pandas to analyze different data sets by coming up with metrics in determining a good neighborhood.
The following datasets were used:
- Pittsburgh Police Arrest Data
- Pittsburgh Police Firearm Seizures
- Median Home Values via the Pittsburgh SNAP Census Data
We have determined that Regent Square is the best neighborhood to live in. This conclusion was made based on the following metrics: number of arrests, gun seizures, and a neighborhood's median home value. Less arrests means less crime in the neighborhood as fewer people are being jailed. Fewer gun seizures also leads to less gun violence in the neighborhood. The neighborhoods that have low values in these metrics are safer to live and therefore better. Neighborhoods with high median home values imply that the location, condition of the neighborhood, or the houses themselves are favorable. All these factors that are implied through the median home value metric make a neighborhood look appealing. Regent Square was the best in all of the individual rankings out of all of our candidates, with only 1 more arrest than Shadyside (26 arrests). Therefore, we decided that Regent Square is the best neighborhood in Pittsburgh.