This branch contains an example of a complex codespace with a large collection of libraries, modules and tools which together form an environment where a machine learning application can be developed with little to no initial setup effort needed.
This codespace was not created from scratch, but based on the
python-3-anaconda
codespace from the microsoft/vscode-dev-containers
repository, where codespaces for various types of projects and programming languages are available.
Before setting up your own codespace configuration, it is always a good idea to look for a
publicly-available one which already has at least part of your needs covered (open source FTW!).
If you are lucky enough, you may even find a codespace which already has everything you want —
off the shelf! 😄
There are two projects in this branch: the first one contains a test application (under
test-project) and
the second one contains a Jupyter Notebook document with machine learning models for analyzing the
Titanic dataset (under
titanic).
Create a codespace for this branch by clicking Code > Open with Codespaces > New codespace.
When it is ready, open and run
test-project/hello.py.
This will show you if the codespace has been properly set up and also activate the conda environment which is
necessary for the next stage of the demo.
The second (and more interesting) part of the demo is on
titanic/titanic.ipynb.
This is a Jupyer Notebook document containing two machine learning models used to analyze the
Titanic dataset. It illustrates how well Jupyter Notebook integrates with the web-based version of
Visual Studio Code and how the codespace enables the data analysis to be carried out without any
setup effort needed from the user.
NOTE: The analysis in
titanic/titanic.ipynb
closely follows this tutorial.
Each step is thoroughly explained there, so follow the link if you wish to understand things in more
detail.