First I want to say I'm completely new to the machine learning paradigm and have only discussed it in theory. I have been trying to put it into practice but I'm confused on how to derive the dataset in a way that would allow me to reverse engineer the following problem:
Let's say we have a dataset with a few traits(attributes for a player) and we are trying to reverse engineer the formula for deciding if a player scores a goal:
Let's say the attributes are the following. All numbers are from 0-100.
AGI AWR KP KA Tec(technique) Player 1 44 60 90 70 66
Let's say the real formula we are trying to reverse engineer is the following:
.25*AGI + .15*AWR + .30*((KP+KA)/2) + .30*(Tec) + diceRoll(3)
And let's say if the number comes out to be greater than 85 the player scores the goal.
Let's say our data set essentially has a bunch of players kick attempts at the goal, and has a true or false for score like the following:
AGI AWR KP KA Tec Score 60 40 70 30 50 1(true) 44 60 90 70 66 0(false) 90 90 60 65 38 0(false)
Is there a way to train a neural network that essentially predict outcomes based on a player's attributes?
Is this the correct use of a neural network? or is there a better tool suited to figure this out?