ee1e8abd87
* feat(tools): add seed/solution restore script * chore(curriculum): remove empty sections' markers * chore(curriculum): add seed + solution to Chinese * chore: remove old formatter * fix: update getChallenges parse translated challenges separately, without reference to the source * chore(curriculum): add dashedName to English * chore(curriculum): add dashedName to Chinese * refactor: remove unused challenge property 'name' * fix: relax dashedName requirement * fix: stray tag Remove stray `pre` tag from challenge file. Signed-off-by: nhcarrigan <nhcarrigan@gmail.com> Co-authored-by: nhcarrigan <nhcarrigan@gmail.com>
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id, challengeType, dashedName
| id | challengeType | dashedName |
|---|---|---|
| 5e46f8e3ac417301a38fb92f | 10 | book-recommendation-engine-using-knn |
--description--
In this challenge, you will create a book recommendation algorithm using K-Nearest Neighbors.
You will use the Book-Crossings dataset. This dataset contains 1.1 million ratings (scale of 1-10) of 270,000 books by 90,000 users.
You can access the full project instructions and starter code on Google Colaboratory.
After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link."
We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project.
--hints--
It should pass all Python tests.
--solutions--
# Python challenges don't need solutions,
# because they would need to be tested against a full working project.
# Please check our contributing guidelines to learn more.