Inspiration The gentrification of lowly-income areas was harming people with low annual wages. So, my team worked to help them make better decisions to search for their homes

What it does It calculates the gentrification probability of a particular area in the Zillow Listing, which will show how much that area is prone to gentrification which will help make better real estate decisions

How I built it: My Team built a prototype using a Discord design template as the form that asks for your background like Age, annual income, and No of people in a household since these are the key factors that are responsible for gentrification and uses a machine learning algorithm to calculate the gentrification of the area with respond to the user's background.

Challenges I ran into: We were not able to get the census API from the US census, so we had to build dummy data to prove that it is possible to calculate gentrification with the machine learning algorithm.

Accomplishments that I'm proud of: The design that I made for the project is something I am really proud of.

What I learned: I learned about handling APIs, and Machine learning to calculate the provided information into something useful added up with web scraping

What's next for Surety: Next Surety will not just remain a prototype but we will be adding funds to this making it a real Surety

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