Built-in Datasets in Python

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.

Datasets in ‘sklearn’

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.

There are other attributes present as well, such as _make_blobs, make_biclusters, make_circles _and so on that come handy for plotting and visualizations.

text-analytics image-processing built-in-data python data analysis

Pattern is an open-source python library and performs different NLP tasks. It is mostly used for text processing due to various functionalities it provides. Text Processing mainly requires Natural Language Processing( NLP), which is processing the data in a useful way so that the machine can understand the Human Language with the help of an application or product. Using NLP we can derive some information from the textual data such as sentiment, polarity, etc.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

For Big Data Analytics, the challenges faced by businesses are unique and so will be the solution required to help access the full potential of Big Data.

Many a time, I have seen beginners in data science skip exploratory data analysis (EDA) and jump straight into building a hypothesis function or model. In my opinion, this should not be the case.

🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...