Scikit-learn is an open-source Python library for machine learning. It provides a wide range of algorithms and tools for data mining and analysis, including:

1. Classification: algorithms for predicting class labels (e.g., decision trees, logistic regression, support vector machines)
2. Regression: algorithms for predicting continuous targets (e.g., linear regression, support vector regression, random forests)
3. Clustering: algorithms for grouping similar data points together (e.g., k-means, spectral clustering)
4. Dimensionality reduction: algorithms for reducing the number of features in a dataset (e.g., principal component analysis, singular value decomposition)
5. Model selection: tools for evaluating and comparing the performance of different models
6. Preprocessing: techniques for preparing data for modeling (e.g., scaling, imputation, feature extraction)

Scikit-learn also provides a variety of utilities for working with datasets, including functions for loading and manipulating data, and for visualizing the results of analyses.

In addition to its machine learning capabilities, scikit-learn also includes functions for performing scientific computing tasks, such as numerical optimization, linear algebra, and statistics.

Overall, scikit-learn is a comprehensive and widely-used library that provides a wide range of tools and algorithms for data mining and machine learning tasks.

• NumPy: Base n-dimensional array package
• SciPy: Fundamental library for scientific computing
• Matplotlib: Comprehensive 2D/3D plotting
• IPython: Enhanced interactive console
• Sympy: Symbolic mathematics
• Pandas: Data structures and analysis