Team: 22
School: Justice Code/International/Harrison
Area of Science: Machine Learning
Interim: Problem Definition: Some players are unable to play at their full potential with where they are placed by a coach. For example, a 6 foot player would be better playing the point guard rather than a center.
Problem Solution: This machine will help coaches have a better idea of when and where to put players in the game so they can play at their best.
Progress to Date: Right now we are working with python. This is our first time using python so we have been learning the basics. We are also experimenting with sklearn, a machine learning software that finds patterns in numbers and values. Once a user inputs a set of numbers, the software will output what it thinks the value of those numbers are. For instance, if I said the numbers 1-3 had a value of 0, and the numbers 8-10 had a value of 100. The machine would recognize the pattern that the lower numbers have a low value and the high numbers have a high value. If I give it an input of 7, the machine would recognize that 7 is closer to the higher set of numbers thus giving it the value of 100.
Expected Results: We expect to be able to give the computer multiple inputs of sport player “ratings†and have it put each player at their full potential, giving the team the highest value possible.
Works Cited
https://scikit-learn.org/stable/user_guide.html
Team Members:
Isaac Rankin
Mclight Emma-Asonye
Sponsoring Teacher: Caia Brown