Hands-on basic baseline ML

Now that we have a working environment let’s create and step through a Jupyter notebook, implementing and demonstrating the foundational concepts we learned in our previous chapter. The notebook is called basic-ml.ipynb and uses Wine, a sample dataset that comes with scikit-learn. The Wine dataset has 13 different attributes of a wine sample and an associated classification (Class_1, Class_2, Class_3). We will first use the sample dataset to show some basic data exploration techniques, such as printing feature names, target labels, target names, and a data preview:

from sklearn import datasets
import numpy as np
# Load the wine dataset
wine = datasets.load_wine()
# Convert to pandas DataFrame
df = pd.DataFrame(data=np.c_[wine['data'], wine['target']], columns=wine['feature_names'] + ['target'])
print("Features:",df.columns.tolist()[::-1])
print("Targets:",df.target.unique());
print(&quot...