The program we made uses random forest regression to judge what aspects of a city would be most benificial to spend additional funding on. The user inputs a city and a list of statistical attributes of the city, then the program outputs which attribute would be most beneficial to the city if it was increased or decreased. With outcomes such as increasing city income, decreasing the city's murder rate, and decreasing the rate of depression.
ChristopherGronewold/Hackku2022-City-Machine-Learning
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