To recreate the environment; run in a terminal:
conda env create -f environment-windows.yml
conda env create -f environment.yml
or
conda create --name <env-name> python=3.8
conda activate <env-name>
conda install -c conda-forge imbalanced-learn=0.9.1
conda install pandas=1.2.5
Then copy and paste this project's [src, res] directories into your newly created anaconda environment.
New ML classification models can be trained, and existing models can be used to predict
labels for new samples.
Pre-existing models are stored in the res/model directory,
datasets are stored in the res/data directory,
and classification reports are stored in the res/report directory.
To train a new model; run in a terminal:
python src/train.py
To list all arguments:
python src/train.py -h
To classify samples using an existing model; run in a terminal:
python src/classify.py
To list all arguments:
python src/classify.py -h