Would a decision tree classifier be applicable here?
As explained in here, decision trees have their disadvantages of a high chance of overfitting, instability with little variance, and low bias. Moreover, considering the fact that your data appears to be numerical, and continuous, there's a high chance that the decision tree might calculate the entropy of your features while only considering scenarios in your training data. 

Decision trees tend to perform better in the scenarios where you have repetitive scenarios, and where your data is pretty much correlated, and most importantly, where you have discontinuous, categorical data in your features, which certainly isn't the case for the data which you mentioned.