I'm looking for information on how should a Python Machine Learning project be organized. For Python usual projects there is Cookiecutter and for R ProjectTemplate.
This is my current folder structure, but I'm mixing Jupyter Notebooks with actual Python code and it does not seems very clear.
. ├── cache ├── data ├── my_module ├── logs ├── notebooks ├── scripts ├── snippets └── tools
I work in the scripts folder and currently adding all the functions in files under my_module, but that leads to errors loading data(relative/absolute paths) and other problems.
I could not find proper best practices or good examples on this topic besides some kaggle competition solutions and some Notebooks that have all the functions condensed at the start of such Notebook.