Inspiration
We were inspired by a love for statistics, predictive analysis, and the city of Jacksonville
What it does
Our project takes data from several mid sized cities and runs linear regression on several variables to predict housing retention and displacement
How we built it
We utilized several libraries made for cleaning, merging, visualization, and analysis
Challenges we ran into
First, we had to clean the data. Then, in order to find what variables were most worth analyzing, we performed principal component analysis. Then, we performed multiple linear regression
Accomplishments that we're proud of
We created compelling visualizations of the housing displacement crisis in Jacksonville. We also found evidence that access to vehicles may reduce the risk of housing displacement
What we learned
We learned about the importance of data cleaning and methods such as Principal Component Analysis for effective data analysis
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