The Jupyter Notebook in this repo is a quick analysis on data used in a previous class I took at the Unveristy of Michigan. In SIADS 601: Qualitative Inquiry for Data Scientists we were asked to interview candidates and make qualitative deductions off of those interviews surrounding a given dataset (If curious, final report is here which is based on the Affinity Wall). However, the class ended there and we were unable to dive deeper into actually visualizing the quantitative data. For that reason, I am curious to do that here.
The dataset I chose can be found at kaggle.com which provides a dataset of home prices from King County Washington (where I grew up!) from May 2014 to May 2015. It provides date sold with price along with a number of typical columns of information like sqft, beds, baths, grade, condition, year renovated, etc...
I am going to pick both grade (An index from 1 to 13, where 1-3 falls short of building construction and design, 7 has an average level of construction and design, and 11-13 have a high quality level of construction and design) and waterfront (either 0 or 1) to see what trends there are related to sale date and sale price.