Welcome to the Team!

Glad to have you onboard for our predictive modeling project! While you get familiar, here are a few things I'd like you all to brush up on:

1. Linear Regression

For this project, I plan on starting with Linear Regression as it is a fundamental part of machine learning and also a lot easier to grasp and understand.

Resource: Codecademy - Simple Linear Regression Course (free!)

2. Git/GitHub

I can't stress this one enough. Understanding how to use Git is absolutely fundamental — so much that I actually want to take the time during our next standup to show you all the workflow we're looking for.

IF YOU'RE UNFAMILIAR WITH GIT: Please check out this resource, it's a great interactive tool that will teach you what "git" is and how crucial it is in our project - Learn git branching

General workflow:

create feature branch → make edits → pull from main → open pull request → merge → repeat

Don't forget to pull from main regularly to keep up to date

Important: Commit Message Standards

Please follow this format for all commit messages:

[COMMIT TYPE]: [COMMIT MESSAGE]

Commit types:

• feat - New features
• fix - Bug fixes
• doc - Documentation changes
• refactor - Code refactoring
• test - Test additions or modifications

Example: feat: add data preprocessing pipeline

3. Python

Pretty obvious and not too hard. There's lots of great resources out there, but this one is a personal favorite of mine:

Resource: futurecoder.io

4. CI/CD and Code Quality Tools

We have automated code quality checks that run on every pull request. Here's what you need to know:

What Gets Checked Automatically

Every time you open a PR, GitHub Actions will automatically run:

• Ruff format - Ensures code formatting consistency
• Ruff lint - Checks code quality and style
• mypy - Type checking for better code reliability
• bandit - Security scanning
• pytest - Runs all tests

Setting Up Your Local Environment

Install the development tools:

pip install ruff mypy pytest bandit pre-commit

Install pre-commit hooks (highly recommended - runs checks before each commit):

pre-commit install

Running Checks Locally

Before pushing your code, you can run these commands to catch issues early:

ruff format .          # Auto-format your code
ruff check --fix .     # Lint and auto-fix issues
mypy .                 # Check types
pytest                 # Run tests

Or run all pre-commit hooks manually:

pre-commit run --all-files

Writing Tests

All tests go in the tests/ directory. Example test structure:

def test_data_preprocessing():
    """Test that preprocessing removes null values."""
    raw_data = load_sample_data()
    processed = preprocess(raw_data)
    assert not processed.isnull().any().any()

For ML projects, focus on testing:

• Data preprocessing functions
• Model input/output validation
• Feature engineering logic
• Edge cases and data quality

This is to be updated...

5. What's Next?

TBD!