• Use Git and GitHub
• Work with Python files (and not just notebooks!)
• Do a pull request on a GitHub repository
• Format your code properly using standard Python conventions
• Make your code pass tests run automatically on a continuous integration system (GitHub actions)
• Understand how to code scikit-learn compatible objects.

• Fork the repository by clicking on the Fork button on the upper right corner
• Clone the repository of your fork with: git clone https://github.com/MYLOGIN/datacamp-assignment-sklearn (replace MYLOGIN with your GitHub login)
• Create a branch called MYLOGIN using git checkout -b MYLOGIN
• Make the changes to complete the assignment. You have to modify the files that contain questions in their name. Do not modify the files that start with test_. - Check locally that your solution meet the test by running pytest from the root of the repo. You may need to install pytest using pip or conda.
• Check the code formating for your solution using flake8. You may need to install flake8 using pip or conda.
• Open the pull request on GitHub:
  • Create a commit with git add -u and git commit -m "UP my solution"
  • Push your branch on your fork: git push -u origin MYLOGIN
  • Go to your repo in your browser and click the Open a PR button.
• Keep pushing to your branch until the continuous integration system is green.
• When it is green notify the instructors on Slack that your done.

• You should implement a scikit-learn estimator for the KNearestNeighbors class. This corresponds to implementing the methods fit, predict and score of the class in sklearn_questions.py.
• You should implement a scikit-learn cross-validator for the MonthlySplit class. This corresponds to implementing the methods get_n_splits and split of the class in sklearn_questions.py.

If you need help ask on the Slack of the Datacamp course.