Parking forecasting
Parking forecasting pipeline for prediction of available parking spaces
To use with docker
docker build -t forecast .
docker run --name parking -d forecast
docker logs -f [Container ID]
File Structure
├── .gitignore <- Files that should be ignored by git. Add seperate .gitignore files in sub folders if
│ needed
|
├── LICENSE
|
├── README.md <- The top-level README for developers using this project.
|
├── setup.py <- Metadata about your project for easy distribution.
│
├── requirements.txt <- The requirements file for reproducing the analysis environments
│
├── conda_env.yml <- Conda environment definition for ensuring consistent setup across environments
|
├── logs <- ML model logs
|
├── data
│ ├── interim_[desc] <- Interim files - give these folders whatever name makes sense.
│ ├── processed <- The final, canonical data sets for modeling.
│ ├── raw <- The original, immutable data dump.
│ ├── temp <- Temporary files.
│ └── training <- Files relating to the training process
│
│
├── src <- Code for use in this project.
│ └── PMV4Cast <- Example python package - place shared code in such a package
│ ├── __init__.py <- Python package initialisation
│ ├── examplemodule.py <- Example module with functions and naming / commenting best practices
│ ├── features.py <- Feature engineering functionality
│ ├── io_data.py <- IO functionality
│ ├── ml_model.py <- Machine learning model
│ ├── simple_average.py <- Baseline average forecasting model (next day with 10 min resolution)
│ └── pipeline.py <- Pipeline functionality
│
└── tests <- Test cases (named after module)
└── PMV4Cast <- PMV4Cast tests
├── examplemodule <- examplemodule tests (1 file per method tested)
├── features <- features tests
├── io <- io tests
└── pipeline <- pipeline tests