devML

repo for ML related projects and tests

Getting Started with a Python Model

note some steps may not yet exist or be implemented (e.g. check results directory) but left here for reference as this repo is improved.

To get started with a Python machine learning model, follow these steps:

Prerequisites

Ensure you have the following installed:

• Python 3.8 or higher
• pip (Python package manager)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/devML.git
   cd devML

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Upgrade Packages

   pip install pip-review
   pip-review # similar to `pip list --outdated`
   pip-review --auto #upgrade all because pip is not easy for this
   pip freeze > requirements.txt # update

5. Install internal modules Modules like utils are self contained modules that live within this project alone to help with helper methods like monitoring. This project follows a flat structure so it remains flexible to organizing. But custom modules like utils requires treating this project like a package and install it on the python interpreter local to this project via venv above. Simply run:

   pip install -e .

Restart the notebook kernel. Then open a file using it such as explorations/utils_checks.ipynb and run the cell that imports it to test.

Running a Model

1. Prepare your dataset and place it in the data/ directory.
2. Run the model script:

   python src/train_model.py

3. Check the output in the results/ directory.

Notes

• Modify config.yaml to adjust model parameters.
• Refer to the docs/ folder for detailed explanations of each module.