Predict the scores for NFL Game

1. Set up a data science project structure in a new git repository in your GitHub account
2. Pick one of the game data sets depending your sports preference https://github.com/fivethirtyeight/nfl-elo-game https://github.com/fivethirtyeight/data/tree/master/mlb-elo https://github.com/fivethirtyeight/data/tree/master/nba-carmelo https://github.com/fivethirtyeight/data/tree/master/soccer-spi
3. Load the data set into panda data frames
4. Formulate one or two ideas on how feature engineering would help the data set to establish additional value using exploratory data analysis
5. Build one or more regression models to determine the scores for each team using the other columns as features
6. Document your process and results

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.

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Project based on the cookiecutter data science project template. #cookiecutterdatascience