Kaggle Code

Kaggle Code is one of the most active sections on the platform. Older names for Code are Kernels and Notebooks and you will frequently hear them used interchangeably. The number of current contributors, at the time of writing this book, exceeds 260,000 and is surpassed by only the Discussions section.

Code is used for the analysis of datasets or competition datasets, for preparing models for competition submissions, and for generating models and datasets. In the past, Code could use either R, Python, or Julia as programming languages; currently, you can only choose between Python (the default option) and R. You can set your editor as Script or Notebook. You can choose the computing resource to run your code, with CPU being the default.

Alternatively, you can choose between four options of accelerators if using Python as a programming language or two if using R. Accelerators are provided free of charge, but there is a quota, reset weekly. For high-demand accelerator resources, there might also be a waiting list.

Code is under source control and, when editing, you can choose to just save (and create a version) or save and run (and you create a code version and a run version). You can attach to Code datasets, Competitions datasets, and external utility scripts and models. As long as you are not rerunning the notebook, changes made in the resources used will not affect its visibility. If you try to rerun the code and refresh the datasets or utility script versions, you might need to account for changes in those data and code versions. The output of code can be used as input to other code, in the same way as you include datasets and models. By default, your code is private, and you do not need to make it public to submit the output to a competition.

If you make your code public, you can get upvotes, and these count for both the ranking in the Notebooks category as well as for getting medals. You need 5 bronze medals for the Expert tier in Notebooks, 10 silver medals for the Master tier, and 15 gold medals for the Grandmaster tier. One bronze medal needs 5 upvotes, a silver medal needs 20 upvotes, and a gold medal requires 50 upvotes. Upvotes in Notebooks can be revoked, and you can also make your public notebooks private again (or delete them). In such a case, all upvotes and medals associated with that Notebook are no longer counted for your ranking or performance tier. There are Code sections associated with Competitions, Datasets, and Models. At the time of writing this book, there were 125 Notebook Grandmasters and 472 Masters.

Kaggle grows and changes continuously, both as a data and competitive machine learning platform and as a community. At the time of writing this book, starting with the new 2023 Kaggle AI Report, Kaggle introduced a review system for Notebook competitions where all participants submitting an essay are also asked to provide a review for another three participants’ essays. The final decision about which submission will win the competition is taken by a panel of experts from veteran Kaggle Grandmasters.

Kaggle Code’s many features and options will be described in the next chapter in a more detailed manner.