Platform:
Organizers:
Competition Page:

• Related docs such as references, articles, documentation, etc to be saved in this directory.

• Input data folder.

• Trained weights/ pretrained-weights of various models used/referred in the solution.

• EDA, modelling, pipeline notebooks to be added here.

• contains the output data such as submission files, etc.

• Code package, consisting of modularised code for data preparation, cross validation, feature engineering, modelling, inference code snippets.

For the starter, I've used pip over miniconda for package management. Follow the steps mentioned to set up your work ennvironment and get going.

1. Create a virutal environment using python venv

cd proj-dir
python -m venv your-environment-name

2. On successful creation of the env, activate the env by navigating to the activate script depending on your operating system.
   If using WSL on windows or linux, run the following:

cd your-environment-name
source bin/activate

To make sure you're using the correct python env, run the following command. You should get the bin/python path of your virutual env as output.

which python

3. Now that the env is there, time to install some initial packages to get going. Note that, you can and should install your project specific requirements as you proceed.

To install from the requirements.txt file:

python -m pip install -r requirements.txt

Note that, it's recommended to follow the given directory structure. Meaning, src, logs, etc. along with your venv folder should be a subfolder inside your proj-dir folder. And all the files should be placed accordingly.

4. Now that the initial libs are installed, you can run the logger and the configuration to make sure things are in-place and working. And, execute the first cell of the starter notebook as well, you should get the config loaded in there.

To run logger and the configuration for sanity check:

cd src
python logger.py
python configuration.py

Note that it's only a starter package for structuring a competitive machine learning or a research project. You are more than welcome to make modifications and recommend the same via pull requests.

Raise a PR for any suggestions/recommendations on frequenctly used practices in competitive machine learning or research projects.