APAQ (AirPort Air Quality) Predictor

The AirPort Air Quality (APAQ) Predictor is an API tool that can be used to predict the air quality (particulate matter with diameter less than 10 microns, PM10) in the area near an airport from an aerial image (synthetic or real) of the airport alone.

Quick Setup

1. Create a virtual environment by executing make in the root.
2. Activate the virtual environment by executing . venv/Scripts/activate (for Windows) or . venv/bin/activate (for Mac/Linux) also in the root directory.
3. Run the program by executing make run in the root directory.

Pre-commit hooks

We use pre-commit hooks and pre-commit will be installed when you run pip install -r requirements-dev.txt. Run pre-commit install to ensure pre-commit hook run whenever you make a commit. Check that you are able to run all hooks locally by running pre-commit run --all-files

Train a Random Forest Model

1. Execute: python -m src.model.main --airports_augmented_dataset DATA_FILE_PATH where DATA_FILE_PATH is the local path to the training dataset
2. The trained model will save as a pickle file automatically to ./model/rf_model.pickle.