We have 2 buskers on the team (a busker is a street musician). While there are extremely successful street musicians, most are encounter difficulties due to 1) lack of skill or 2) tipping-averse audiences. While we can't do much for the former, spending propensity in any area is easily modeled by using transaction data. In order to help our fellow buskers, we built an app that points out the best areas to visit.
What it does
Buskr uses Capital One's Enterprise API to get information on transactions, including amount, date, and geographic location. Using a simple predictive ML model, Buskr creates a "spend propensity" index per person. Buskr then generates a user-friendly heat map of predicted tip revenue using these spend propensities and directs the user to the nearest "hot spot."
How we built it
Challenges we ran into
Capital One's randomized data had a few identification issues which presented issues when we tried to join tables.
What we learned
What's next for Buskr
Given more time, we would've introduced an online tipping feature for business to use to incentivize buskers to perform near their area.