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.

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Scikit-Learn
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