As a last learning resource, the Scikit-learn package also offers the possibility to quickly create synthetic datasets for regression, binary and multilabel classification, cluster analysis, and dimensionality reduction.
The main advantage of recurring synthetic data lies in its instantaneous creation in the working memory of your Python console. It is, therefore, possible to create bigger data examples without having to engage in long downloading sessions from the internet (and saving a lot of stuff on your disk).
For example, you may need to work on a classification problem involving a million data points:
In: from sklearn import datasets X,y = datasets.make_classification(n_samples=10**6, n_features=10, ...