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Industry

All Industries

Language

Python, Cython

Features

Data Wrangling, Data Mining, Text Processing, Machine Learning, Modeling

Scikitlearn focuses on effective implementations of widely used machine learning algorithms, with a focus on supervised and unsupervised learning. In particular, it allows practitioners to quickly build complex machine learning pipelines and easily swap out different models. Scikitlearn is built on top of numpy arrays, and therefore focuses on in-memory models of homogeneous data, though some support for out-of-core computations and heterogeneous data exist. Implementations rely either on vectorized computations with numpy, or efficient low-level implementations in Cython.

Scikitlearn is widely used across industry and research. Applications range from finding exoplanets to fraud detection in credit card transactions to analyzing brain imaging data. Scikitlearn is used at tech companies such as Amazon and Microsoft as well as in manufacturing processes in companies like Mars, Inc.

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