Kaggle notebooks are part of a data ecosystem that includes Kaggle servers, Kaggle Notebooks, Kaggle Datasets, Kaggle Dashboards, and Kaggle Colab notebooks. Using Kaggle notebooks as placeholders for machine learning code makes your work reusable across a wide range of machine learning applications.
Create your own Kaggle notebooks with notebooks.kaggle.com
In this post, we'll use notebooks.kaggle.com to explore the code of a few Kaggle notebooks, including one of our own, using Jupyter Notebook. This code is modeled after Jupyter Notebook syntax, but is not limited to Jupyter Notebook.
Overview of the Kaggle Notebook Ecosystem
Kaggle notebooks are free, open-source code repositories that contain visualizations, experiments, and machine learning code. They are created as placeholders for machine learning models and applications. You can write code in the notebooks, which helps you explore how machine learning models work, and share the code with others.
While it's helpful to think of notebooks as placeholders for code, you should not use a notebook to write out the code that you want to run. Instead, write the code out in a Jupyter Notebook and then save it to a Kaggle Notebook. You can then run the code in a Kaggle Notebook server or use the code in a Jupyter Notebook as a placeholder for a machine learning application.
An Example of a Kaggle Notebook
In this section, we will create and use our own Kaggle Notebook, which can be found on Github. A Kaggle Notebook is just like a Jupyter Notebook, but it uses Kaggle Notebook syntax and handles CSV files.
We will create a Kaggle Notebook, which we will use to explore and run machine learning code on data from Kaggle Datasets.
Navigate to notebooks.kaggle.com to create your own Kaggle Notebook.
Note that we will use an SSH account to connect to the notebook's code.
GitHub - 
Create a Kaggle Notebook
To create our own Kaggle Notebook, navigate to 3125841983
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