As a Data Scientist, we would like to experiment with many different packages and codes to create that amazing analysis and superb machine learning model. The problem with this kind of experiment is that many packages dependencies overlap one another, and sometimes it disturbs our working environment. In this case, we need what we called Virtual Environment.

A virtual environment, just as the name implies, is an environment that is virtually created. It is an empty place isolated from our local source where we could install and play around with code and packages.

It is a best practice to use Virtual Environment where we are testing a new package or creating a new pipeline.

While it is a best practice, sometimes people wonder how we implement the newly created virtual environment we create to be used in the Jupyter Notebook. In this case, I want to show you how to create and use the virtual environment in Jupyter Notebook.

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