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Expand Up @@ -123,8 +123,6 @@ Click on the double arrow (⏩️) to execute the Python scripts (click on the i

image::jupyterhub-pyspark-hdfs-anomaly-detection-taxi-data/jupyter_hub_run_notebook.png[link=https://github.com/stackabletech/demos/blob/main/stacks/jupyterhub-pyspark-hdfs/notebook.ipynb,window=_blank]

You can also inspect the `hdfs` folder where the `core-site.xml` and `hdfs-site.xml` from the discovery ConfigMap of the HDFS cluster are located.

The Python notebook uses libraries such as `pandas` and `scikit-learn` to analyze the data.
In addition, since the model training is delegated to a Spark Connect server, some of these dependencies, most notably `scikit-learn`, must also be made available on the Spark Connect pods.
For convenience, a custom image is used in this demo that bundles all the required libraries for both the notebook and the Spark Connect server.
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