In the context of this exercise, what other datasets does the scikit-learn library provide?
In addition to the Iris dataset that you will work with in this lesson, the scikit-learn library includes several other datasets that are useful when testing out the functionality of data science and machine learning libraries.
Another one of the available datasets is the Boston house prices dataset, which includes parameters such as property-tax rates and the average number of rooms per dwelling. You can access this using the following,
from sklearn import datasets boston = datasets.load_boston()
Another dataset provided in scikit-learn is the Diabetes dataset, which includes information for diabetes patients such as their age, sex, body mass index, and average blood pressure. You can access this dataset using,
from sklearn import datasets diabetes = sklearn.load_diabetes()
One other dataset available in the library is the Wine recognition dataset, which includes information about wines including their alcohol content, color intensity, and hue. This dataset can be obtained using,
from sklearn import datasets wine = sklearn.load_wine()
For a list of all the other available datasets you can check out the scikit-learn documentation.