Scikit-learn is a free Python package that is also written in Python. Scikit-learn provides a machine learning library that supports several popular machine learning algorithms for classification, clustering, regression, and so on. Scikit-learn is very helpful for machine learning novices. Scikit-learn can be easily installed by running the following command:

pip install sklearn

To check whether the package is installed successfully, conduct a test using the following piece of code in Jupyter Notebook or the Python command line:

import sklearn

If the preceding argument throws no errors, then the package has been successfully installed.

Scikit-learn requires two dependent packages, NumPy and SciPy, to be installed. We will discuss their functionalities in the following sections. Scikit-learn comes with a few inbuilt datasets like:

• Iris data set
• Breast cancer dataset
• Diabetes dataset
• The Boston house prices dataset and others

Other public datasets from libsvm and svmlight can also be loaded, as follows:

http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/

A sample script that uses scikit-learn to load data is as follows:

from sklearn.datasets import load_boston
boston=datasets.load_boston()