I’d love to see the step by step walkthrough on doing this step if you do have a data source.This will not run a data source, but it will allow you to run machine learning code on your data, which can help you discover what’s going on and figure out how you can improve what you’ve got.
1. In the Google Cloud Platform console, navigate to the Kaggle Studio page.
In the Firebase console, navigate to Kaggle Studio.
After a moment, Kaggle Studio should appear.
2. Click the Login button on the Kaggle Studio page.
After a moment, a login window will appear.
3. Login to Google with your personal account.
After a moment, you will be logged in.
If you are experiencing issues logging in to Kaggle, check the Google Cloud console to ensure you are logged in with the correct credentials.
4. Click the Download Data button.
Click the Download Data button, and select the directory where you want the Google BigQuery data downloaded to.
NOTE: You cannot directly download the Google BigQuery data to your local file system.
This step is only used for debugging purposes. It should not be used to download data to your local computer.
5. On the Kaggle Studio page, click the name of your experiment.
Click the name of the experiment that you want to use to run code against.
For this walkthrough, we will create a new experiment called NumPy_pipeline_examples.
6. Click the Open button to the right of the experiment.
Click the Open button to the right of the experiment.
A popup window will open.
7. For the Kaggle project id, enter the Kaggle project id you created in Step 1.
For the project id, enter the Kaggle project id you created in Step 1.
8. For the Kaggle experiment id, enter the name you gave to the experiment created in Step 5.
For the experiment id, enter the name you gave to the experiment created in Step 5.
9. Click Continue.
Click Continue.
You will see the experiment page.
10. In the Experiment Overview page, enter the name of the notebook to run the code in.
In the
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