In this exercise you will practice working with sklearn pipelines. You will go through some of the typical steps in the ML model lifecycle:
- data loading
- data exploration
- splitting the data into train and test
- creating a model
- evaluating the model
You will be working with datasets related to wine quality. Each item in a dataset corresponds to a wine; based on its features, such as acidity, sugar levels, density, your model will predict the quality rating of a wine.
Click on <>Code and then Create codespace on main. When the codespace finished building the dependencies in requirements.txt should be installed.