We want to explore various factors that may impact poverty levels in Champaign county. In doing so, we identify areas in which more resources need to be allocated to help alleviate the issue.
While researching, we also discovered a disturbing truth: the Champaign area has one of the highest poverty rates in all of Illinois.
We trained a random forest classifier on data provided by Champaign County Regional Planning Comission, using cities, towns, and municipalities as data points, and key features such as:
- Average Household Size
- Language Most Often Spoken at Home
- Mean Travel Time to Work
- Assessed population
to generate a model that would identify the most important features that influence the poverty rate. We found that, (after median household income), the most influential features were average household size and language most often spoken at home.
We hope our approach can be useful for iterative reductions to poverty, by allocating more money to the most influential factors first.
Website: cu-poverty.ml (Down since Nov. 09, 2019)
In Progress
- Aggregate all data in one location
- train the model on the data
- Create a website
- Host the website off a service such as Azure or AWS, and use their ML capabilities to run the classifier on the cloud as well
Website to-do list:
- Host on Azure and allow ML python file to actually run when CSV is sent (Website is now down)
- Add more info on the process, just expand on everything and go into more detail on the website - make the words better as well
- Add more graphs/visual aids, edit existing graph colors, font sizes, etc.
- clean up code - a lot of what we have is unnecessary
- Make sure it looks good/works on mobile