ML App template

How to get the dataset

@TODO

Local installation

python -m venv dev
source dev/Scripts/activate
pip install -r requirements.txt

Docker installation

Build the image

docker build --tag app:1.0 .

Access the REST API

docker run --publish 8501:8501 -it app:1.0 src/api.py

Then access http://localhost:8501

Everytime you update the project, you must build a new image with a new tag.

Train the model

1. Download the RAW data ;
2. Execute src/clean.py to create cleaned_data.pkl ;
3. Execute src/feat_prep.py to create training.pkl ;
4. Execute src/create_folds.py to create training_folds.pkl ;
5. Execute src/train.py to train the model ;

Evaluate the performance of the model

python src/report.py --fold=1

fold value is in range [0,4]

Quality tools

python -m isort src/
python -m black src/
python -m flake8 src/ --count --statistics

LICENSE

This project is provided under the MIT license.