Built-in datasets prove to be very useful when it comes to practicing ML algorithms and you are in need of some random, yet sensible data to apply the techniques and get your hands dirty.
Python modules containing built-in datasets and ways to access them
IRIS types (Source: DataCamp)
Built-in datasets prove to be very useful when it comes to practicing ML algorithms and you are in need of some random, yet sensible data to apply the techniques and get your hands dirty. Many modules in python house some common datasets of the likes of the popular ‘Iris’ data. In this article, we will see the datasets available within ‘sklearn’ and ‘statsmodels’ modules, and ways to access the data and related info. Short demonstrations to load a dataset each for classification, text analytics, image processing and time series analysis is provided.
To see the list of datasets provided by Sci-kit learn module, execute the below command.
from sklearn import datasets dir(datasets)
You will get the directory of contents available in ‘datasets’ of which below are the ones containing data that can be used for regression, classification, text analysis and image processing. For example, ‘_20newsgroups_’, ‘_20newsgroups_vectorized_’ are for text analytics; ‘_california_housing_’ for regression; ‘_digits_’ for image processing; ‘_iris_’, ‘_wine_’ for classification.
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