This is the repository for the final project in Professor Arjun Chandrasekhar's CMPINF 0010 course. We are tasked with finding the best neighborhood in Pittsburgh based on datasets from the WPRDC database, and using the Pandas data visualization library of the Python language to communicate our results.
For our project, we picked three factors that we thought were most important for the determination of a good neighborhood. The Pittsburgh SNAP Census datasets (2010) had all the information we needed, indexed by neighborhood. The factors we chose were natural environmental conditions, health/safety, and housing.
These datasets are all from the Pittsburgh SNAP Census data from 2010. Each of these datasets was indexed by neighborhood
https://data.wprdc.org/dataset/pgh/resource/204f63f4-296f-4f1d-bbdd-946b183fa5a0
https://data.wprdc.org/dataset/pgh/resource/94f8eda2-fa77-49a2-9190-3a6ed85fc561?inner_span=True
https://data.wprdc.org/dataset/pgh/resource/9c46a88c-8fca-4839-9848-c2b819ecbf0f?inner_span=True
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