Assignment 3 for the DataCamp course X-DataScience Master - scikit-learn API

What we want you to check that you know how to do by doing this assignment:

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.