Scikit-learn is a machine learning library, written in Python, designed to incorporate classic machine learning algorithms into contemporary data science workflows. Scikit-learn is a free and open source library consisting of simple and efficient tools for data mining and data analysis, designed to work with popular scientific computing languages SciPy and NumPy.
The library is built on existing open source projects such as SciPy, NumPy, pandas, and Cython, and combines historical machine learning research with modern software engineering. It provides a range of supervised and unsupervised learning algorithms.
Available algorithms include logistic regression, random forests, and support vector machines, along with clustering models (e.g. k-means), decision trees, and ensemble methods. Scikit-learn also provides utility functions for visualization, preprocessing, feature selection, and more. It is easily integrated with other libraries, such as Matplotlib and Jupyter notebooks.
Thanks to its API design makes it easy for users to extend the library. Moreover, comprehensive documentation is available for the library as online documentation or tutorials and an ever-growing user community.
Scikit-learn is can be used for a variety of tasks, including classification, regression, clustering, and dimensionality reduction, and can be used to define, train, test, and evaluate machine learning models. It is well suited for small to medium-scale datasets, as well as big data training.
