Homelessness is a larger global issue everyday that required our immediate assistance to address and control. Many global agencies are running out of resources to help the growing number of those in need, resulting in a demand for a solution that will prevent people from entering into they system to begin with. We are proactively accessing a users risk of becoming homeless based on their personal spending habits and personal information that studies have proven lead to a higher risk of homelessness. Because homeless can impact anyone, at any given time, we are confident that the community will be sympathetic to those in need, and willing to donate to their causes to make a difference.

What it does

Allows residents to view their RISK score (Low, Med, High) based on responses. The RISK score is generated using logistic regression model. Residents get actionable steps based on the RISK score allowing them to mitigate it. Allows residents who fall under HIGH risk to create crowd funded financial help. Leverages Beacon technology to enable community members to donate towards these crowd-funding accounts

How we built it

  • RISK SCORE: Generated based on logistic regression data model using R
  • MATCHING DONORS with RECIPIENTS: Match is generated based on proximity sensors via Beacons
  • MOBILE APP: Mobile app is based on Android IDE
  • WEB : BootStrap files, JSON, JS

Challenges we ran into

Team co-ordination Poor documentation on tech stack that we wanted to use Group consensus is HARD

Accomplishments that we're proud of

Team kept chugging on in-spite of challenges

What we learned

EXECUTION is greater than TEAM is greater than IDEA

What's next for @home

See if Los Angeles county and San Francisco would like to use it or a variation of it.

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