Weathering Waves

Inspiration

Throughout the United States, it has progressively become harder and more expensive to become a homeowner. As young people who want to become homeowners later in life, we wanted to create a tool that can help prospective homeowners make more informed decisions.

What it does

Weathering Waves processes and analyzes a database of over 1,000,000 homes provided by Fannie Mae. Users can access this data in the form of graphs generated by our code and examine them for implications of hurricanes on foreclosure rates through heatmaps and bar charts, particularly focusing on Florida.

How we built it

Our project was developed in two main stages. *Initially, we faced challenges in processing the vast dataset efficiently. We began by exploring database processing options like MySQL. *However, we later found that using Python with libraries such as Pandas, Numpy, and Matplotlib in a Jupyter Notebook for data processing and visualization was more optimized for our goals. This allowed us to navigate through the extensive dataset while managing data effectively.

Challenges we ran into

One of the major hurdles we encountered was handling the large dataset provided by Fannie Mae. We initially explored database processing options but eventually settled on Python and its data manipulation libraries. We also spent a lot of time figuring out which aspects of the data to use. Learning to efficiently process and visualize data in Python was a significant learning curve for our team but ultimately contributed to our success.

Accomplishments that we're proud of

We are particularly proud of successfully processing a dataset of over a million lines and effectively learning and utilizing Python for data manipulation and visualization. Additionally, we successfully developed a website using the Mercury framework while scripting in Python. Our proudest achievements stem from the learning experiences gained throughout the project.

What we learned

Our project provided numerous learning opportunities, particularly in mastering Python for data processing and visualization. We also gained valuable insights into frontend and backend development, as well as the challenges of working with large datasets and integrating diverse technologies within a short timeframe.

What's next for Weathering Waves

Moving forward, we aim to expand Weathering Waves into an interactive application that can provide even more representations of the data we have to meet the user’s specific needs. To do this, we plan to add features like the ability to search or filter by location for home safety assessments or mortgage loan data based on location-specific risks from severe weather events.