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MongoDB, Spark, R, Python, scikit-learn and Kaggle Notebooks are all great for exploring Machine Learning (ML) models and data. But, when it comes to running the code in these notebooks, things aren’t quite as easy. Even though some notebooks have included sample code, it’s not always clear how to run the code and get the data to generate the results.
To solve this, we’ve worked with Amazon to create a sample notebook in the official Kaggle Notebooks. By sharing the notebook, the code is open-source and anyone can run the code, submit their own results and build a ML model.
We’re calling it the ML Colab Notebook. It provides guidance on how to use the notebook to run the code and get data to use with it. The notebook is also the same notebook that Kaggle use to validate the results they use to predict the Olympics with.
If you’d like to try it out or play around with it, click the “Try it yourself” link.
You’ll need to do three things.
Download the notebook: Click the “Try it yourself” link
Open the downloaded notebook in Colab.
Copy and paste the code into Colab.
Let’s take a look at the code in the notebook and what it does.
Contents of the Colab Notebook
The notebook has two parts. The top section shows the structure of the code and instructions on how to run it. In the bottom section, there’s a collection of samples and their results.
Instructions
To run the code, you’ll need to copy and paste the code in Colab. You can also right-click the code and choose “run Python file” to open the code in Colab.
Instructions for the top section of the notebook:
Copy and paste the code into Colab
For the code to work, you’ll need to click the “Play” button at the top of the notebook.
Select the “Run” button in the top left of the notebook and select “Run”.
This takes a while to run, so if you’d like to see the notebook
Related links: