Bay Area Safer Housing (B.A.S.H.)

🛟 BASH (Bay Area Safer Housing)

A web app that helps homebuyers understand the safety of neighborhood communities throughout the Bay Area.

🔗 https://devpost.com/software/bay-area-safer-housing-b-a-s-h

💫) Inspiration

The inspiration initially stems from one member of our team, whose mother is currently searching for affordable housing options. We decided to pursue this project as it is widely applicable to many people we already know, and consider this tool to be of high importance for many locals of the Bay Area.

❓) What it does

B.A.S.H. searches for affordable housing options based on input from the user about their financial situation and provides a safety rating based on crime per city. Additionally, it offers information about public initiatives available to residents of the housing found in the search.

🛠️) How we built it

Our website's foundation was built on next.js using React and Tailwind CSS, implementing the Google Maps platform and their API. Using pandas and NumPy, we interact with our datasets of choice.

💢) Challenges we ran into

Our difficulties lay in our inexperience with a lot of the tools we need to use in order to fulfill our purpose. Most of it resulted in technical difficulties implementing our AI, which we ultimately felt shaky on and unsure of leaving in our project for current submission.

🏆) Accomplishments that we're proud of

Conceptually, we believe that our project is meaningful and sound in its usability and utility. This concept is close to home for us.

🧠) What we learned

We've learned most importantly how to better approach a project such as B.A.S.H., as we pivoted our concept after finding our initial idea to be overly ambitious. Additionally, we've gotten much more familiar with our understanding of machine learning, and are continuing to learn about its implementation currently and beyond CalHacks.

⌛) What's next for B.A.S.H.

We are serious in our project and are intent on continuing our progress. Our motivation behind B.A.S.H. leaves us confident and passionate about polishing this tool and we will be leaving CalHacks inspired to make the most out of our experience over this weekend. Next steps for complexity include implementing a machine learning model to label and characterize neighborhoods and leveraging our existing implementation of CockroachDB to analyze more areas around the world; We primarily plan to fully polish our AI implementation, which we struggled to apply during the CalHacks time period. Aside from that, we would like to host this project and make it available publicly once it's ready for public use.

This is a Next.js project bootstrapped with create-next-app.