Training machine learning models on tabular data

In this example, we will use a very popular dataset in data science, which is the wine dataset of physicochemical properties, to predict the quality of a specific wine. We will be using Azure Databricks Runtime ML, so be sure to attach the notebook to a cluster running this version of the available runtimes, as specified in the requirements at the beginning of the chapter.

Engineering the variables

We'll get started using the following steps:

1. Our first step is to load the necessary data to train our models. We will load the datasets, which are stored as example datasets in DBFS, but you can also get them from the UCI Machine Learning repository. The code is shown in the following snippet:

   import pandas as pd
   white_wine = pd.read_csv("/dbfs/databricks-datasets/wine-quality/winequality-white.csv", sep=";")
   red_wine = pd.read_csv("/dbfs/databricks-datasets/wine-quality/winequality-red.csv&quot...