scikit-learn is an open-source Python library for machine learning and data analysis. It provides simple and efficient tools for data mining and data analysis, offering a wide range of algorithms for classification, regression, clustering, and model selection. scikit-learn is widely used by data scientists and machine learning practitioners to develop and evaluate models for various applications.

Features of scikit-learn:

• scikit-learn provides a wide variety of machine learning algorithms for classification, regression, clustering, and dimensionality reduction.
• It includes powerful tools for data preprocessing, including scaling, encoding, and feature extraction, to prepare datasets for analysis.
• scikit-learn offers various model evaluation and validation techniques such as cross-validation and performance scoring metrics.
• It seamlessly integrates with other Python libraries like NumPy, pandas, and Matplotlib, making it a versatile tool in data science workflows.
• scikit-learn is designed for ease of use, with an intuitive API that allows quick implementation and experimentation with machine learning models.

To check the version, run:

 python -c "import sklearn; print(sklearn.__version__)" 

Disclaimer: scikit-learn is an open-source project released under the BSD 3-Clause License. It is not affiliated with, endorsed by, or sponsored by any commercial entity. scikit-learn is provided "as is," without any warranty, express or implied. Users are responsible for understanding and complying with licensing terms and regulations while using scikit-learn.