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# project proposal

< [Back](/tiny-engines/nfl-machine-learning-capstone/main.md)

#### Problem Statement

* We want to decide whether to kick off a project that uses readily available NFL data to apply machine learning to predict NFL game outcomes

#### Context

* The NFL has some good play-by-play data, and we'd like to understand the difficulty and feasibility of using this data as input to a machine learning projects.

#### Criteria for Success

* [ ] Provide a rough level-of-effort to predict NFL game outcomes using machine learning.
* [ ] Create a proof-of-concept to show how data can be formatted as input to a machine learning model.
* [ ] Show that the model can learn to predict NFL game outcomes better than a guess.

#### Scope of solution

* We don't need to predict games accurately. We just need to show that we can train a model with data that we curated to predict NFL game outcomes better than a guess.

#### Constraints

* Because we have 2 weeks to decide whether to green-light a project to predict NFL game outcomes, we will not try to perfect either the model or the data.\
  We'll just see if we can get something working with a single input supervised model. We do not have access to the NFL API, which may provide better data.