Parking forecasting pipeline for prediction of available parking spaces
docker build -t forecast .
docker run --name parking -d forecast
docker logs -f [Container ID]
├── .gitignore <- Files that should be ignored by git. Add seperate .gitignore files in sub folders if
│ needed
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├── LICENSE
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├── README.md <- The top-level README for developers using this project.
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├── setup.py <- Metadata about your project for easy distribution.
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├── requirements.txt <- The requirements file for reproducing the analysis environments
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├── conda_env.yml <- Conda environment definition for ensuring consistent setup across environments
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├── logs <- ML model logs
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├── 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
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├── 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
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└── 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