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My paint skills aren't the best! All I know is theory, so well you'll have to look for the code.. If you have only 1 case(The best for "x" situations) the diagram becomes something like (It won't be 2-D, but something like this): <img src="https://i.stack.imgur.com/Bpwz8.png" alt="The 3 dots"> Green (Win), Orange(Draw), Red(Lose) Now if you want to predict whether the team wins, loses or draws, you have (at least) 2 models to classify: <ol> <li><a href="http://en.wikipedia.org/wiki/Linear_regression" rel="nofollow noreferrer">Linear Regression</a>, the separator is the Perpendicular bisector of the line joining the 2 points: <a href="http://en.wikipedia.org/wiki/Linear_regression" rel="nofollow noreferrer"> <img src="https://i.stack.imgur.com/2qX20.png" alt="Linear Regression"> </a></li> <li><a href="http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm" rel="nofollow noreferrer">K-nearest-neighbours</a>: it is done just by calculating the distance from all the points, and classifying the point as the same as the closest.. <img src="https://i.stack.imgur.com/53wwL.png" alt="Something!!!"> </li> </ol> So, for example, if you have a new data, and have to classify it, here's how: <ol> <li>We have a new point, with certain attributes.. <img src="https://i.stack.imgur.com/r1WNT.png" alt="New Point"></li> <li>We classify it by seeing/calculating which side of the line the point comes in (or seeing how far it is from our benchmark situations... <img src="https://i.stack.imgur.com/fcwiq.png" alt="Classified Point"></li> </ol> <blockquote> Note: You will have to give some weightage to each factor, for more accuracy.. </blockquote>
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1. POMachine Learning in Python - Get the best possible feature-combination for a label
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1. COThanks @amp for K-Nearest Neighbor..
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