Python Tutorial: Fit and evaluate a model
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In this lesson, you will take the model you compiled in lesson two and fit it to college basketball data.
Your goal is to predict which team will win a tournament game.
The only data you have to work with are the team's "seeds", which are assigned by the tournament organizers, and are a rating of how good the team is.
A seed of 1 is a very good team, and a seed of 16 is a very bad team.
In the 30 plus year history of the tournament, a 16 seed has beat a 1 seed exactly once. It was in 2018, which was a very exciting year for college basketball fans.
Your input will be the difference in seed between the two teams. For example, if a 7 seed plays a 10 seed, their seed difference is 7 minus 10, or -3.
If an 11 seed plays a 7 seed, their seed difference is 11 minus 7, or 4.
Your output will be the difference in score between the two teams. For example, if team 1 scores 41 points and team 2 scores 50 points, the score difference is 41 minus 50, or negative 9.
On the other hand, if team 1 scores 61 points and team 2 scores 55 points, the score difference is 61 minus 55 or positive 6.
Therefore, your model has one input, and one output. This is exactly the model you created in lessons 1 and 2 of this chapter!
You will use the difference in seeds as your input.
Note that you have both a 16 seed playing a 1 seed, and a 1 seed playing a 16 seed in your data, so you'll have seed differences ranging from negative 15 to positive 15.
A seed difference of positive 15 means that team 1 has a seed of 16 and is playing a team of seed of 1. This means team 1 is likely (though not certain) to lose.
A seed difference of negative 15 means that team 1 has a seed of 1 and is playing a team of seed of 16. This means team 1 is likely (though not certain) t
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