NFL DPI Prediction

1. Running scripts

Linux

• Create .env from .env_example
• Run ./postactivate.sh to add your directory to python path

Windows

Procedure on windows is a little bit different

• Load root directory to python path with:

conda develop .

Requirements

Running processing and training scripts requires at least 16 GB of RAM.

2. Project Organization

---

├── LICENSE
├── 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.
│
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── 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`.
│
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── nfl_requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         conda list --export > nfl_requirements.txt
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── create_v3_data.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   ├── helpers <- Processing helpers
│   │   ├── plot <- Processing for plotting and visualisation
│   │   └── v3 <- Dataset creation steps
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   ├── ann
│   │   │   ├── models.py <- Model architecture
│   │   │   ├── predict_ann.py <- Model evaluation
│   │   │   └── train.py <- Model training
│   │   ├── gru
│   │   ├── lstm
│   │   ├── mlstm
│   │   └──  grid_search_models.py <- Run grid search on all models
│   │   
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       ├──generate_grid_search_report.py <- Generate 'per model' statistics for trained models
│       └──accumulate_grid_search_report.py <- Gather 'per model' grid search reports into single file
│
└── settings.py            <- Settings file with constants and configuration variables.

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