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Added the "Applications" section which outlines the real-life use cases. All applications were taken from the official Testimonial page (https://scikit-learn.org/stable/testimonials/testimonials.html). It was inspired by the "Applications" section on TenserFlow wiki page. |
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scikit-learn integrates well with many other Python libraries, such as [[Matplotlib]] and [[plotly]] for plotting, [[NumPy]] for array vectorization, [[Pandas (software)|Pandas]] dataframes, [[SciPy]], and many more.
==
scikit-learn was initially developed by David Cournapeau as a Google Summer of Code project in 2007. Later that year, Matthieu Brucher joined the project and started to use it as a part of his thesis work. In 2010, [[French Institute for Research in Computer Science and Automation|INRIA]], the [[French Institute for Research in Computer Science and Automation]], got involved and the first public release (v0.1 beta) was published in late January 2010.
== Applications ==
Scikit-learn is widely used across industries for a variety of machine learning tasks such as classification, regression, clustering, and model selection. The following are real-world applications of the library:
=== Finance and Insurance ===
* '''AXA''' uses scikit-learn to speed up the compensation process for car accidents and to detect insurance fraud.<ref name="sklearn-testimonials">{{Cite web |title=Testimonials |url=https://scikit-learn.org/stable/testimonials/testimonials.html |website=scikit-learn.org |access-date=2025-08-06}}</ref>
* '''Zopa''', a peer-to-peer lending platform, employs scikit-learn for credit risk modelling, fraud detection, marketing segmentation, and loan pricing.<ref name="sklearn-testimonials"/>
* '''BNP Paribas Cardif''' uses scikit-learn to improve the dispatching of incoming mail and manage internal model risk governance through pipelines that reduce operational and overfitting risks.<ref name="sklearn-testimonials"/>
* '''J.P. Morgan''' reports broad usage of scikit-learn across the bank for classification tasks and predictive analytics in financial decision-making.<ref name="sklearn-testimonials"/>
=== Retail and E-Commerce ===
* '''Booking.com''' uses scikit-learn for hotel and destination recommendation systems, fraudulent reservation detection, and workforce scheduling for customer support agents.<ref name="sklearn-testimonials"/>
* '''HowAboutWe''' uses it to predict user engagement and preferences on a dating platform.<ref name="sklearn-testimonials"/>
* '''Lovely''' leverages the library to understand user behaviour and detect fraudulent activity on its platform.<ref name="sklearn-testimonials"/>
* '''Data Publica''' uses it for customer segmentation based on the success of past partnerships.<ref name="sklearn-testimonials"/>
* '''Otto Group''' integrates scikit-learn throughout its data science stack, particularly in logistics optimization and product recommendations.<ref name="sklearn-testimonials"/>
=== Media, Marketing, and Social Platforms ===
* '''Spotify''' applies scikit-learn in its recommendation systems.<ref name="sklearn-testimonials"/>
* '''Betaworks''' uses the library for both recommendation systems (e.g., for Digg) and dynamic subspace clustering applied to weather forecasting data.<ref name="sklearn-testimonials"/>
* '''PeerIndex''' used scikit-learn for missing data imputation, tweet classification, and community clustering in social media analytics.<ref name="sklearn-testimonials"/>
* '''Bestofmedia Group''' employs it for spam detection and ad click prediction.<ref name="sklearn-testimonials"/>
* '''Machinalis''' utilizes scikit-learn for click-through rate prediction and relational information extraction for content classification and advertising optimization.<ref name="sklearn-testimonials"/>
* '''Change.org''' applies scikit-learn for targeted email outreach based on user behaviour.<ref name="sklearn-testimonials"/>
=== Technology ===
* '''AWeber''' uses scikit-learn to extract features from emails and build pipelines for managing large-scale email campaigns.<ref name="sklearn-testimonials"/>
* '''Solido''' applies it to semiconductor design tasks such as rare-event estimation and worst-case verification using statistical learning.<ref name="sklearn-testimonials"/>
* '''Evernote''', '''Dataiku''', and other tech companies employ scikit-learn in prototyping and production workflows due to its consistent API and integration with the Python ecosystem.<ref name="sklearn-testimonials"/>
=== Academia ===
* '''Télécom ParisTech''' integrates scikit-learn in hands-on coursework and assignments as part of its machine learning curriculum.<ref name="sklearn-testimonials"/>
== Awards ==
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* 2019 Inria-French Academy of Sciences-Dassault Systèmes Innovation Prize<ref>{{Cite web |title=The 2019 Inria-French Academy of Sciences-Dassault Systèmes Innovation Prize : scikit-learn , a success story for machine learning free software {{!}} Inria |url=https://www.inria.fr/en/2019-inria-french-academy-sciences-dassault-systemes-innovation-prize-scikit-learn-success-story |access-date=2025-03-19 |website=www.inria.fr}}</ref>
* 2022 Open Science Award for Open Source Research Software<ref>{{Cite web |last=Badolato |first=Anne-Marie |date=2022-02-07 |title=Open Science Awards for Open Source Research Software |url=https://www.ouvrirlascience.fr/open-science-free-software-award-ceremony/ |access-date=2025-03-19 |website=Ouvrir la Science |language=en}}</ref>
==References==
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[[Category:Python (programming language) scientific libraries]]
[[Category:Software using the BSD license]]
[[Category:2010 in artificial intelligence]]
[[Category:2010 software]]
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