At PeerIndex we use scientific methodology to build the Influence Graph - a
unique dataset that allows us to identify who's really influential and in which
context. To do this, we have to tackle a range of machine learning and
predictive modeling problems. Scikit-learn has emerged as our primary tool for
developing prototypes and making quick progress. From predicting missing data
and classifying tweets to clustering communities of social media users, scikit-
learn proved useful in a variety of applications. Its very intuitive interface
and excellent compatibility with other python tools makes it and indispensable
tool in our daily research efforts.
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Ferenc Huszar - Senior Data Scientist at Peerindex
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:target: https://www.brandwatch.com/peerindex-and-brandwatch
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`DataRobot `_
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DataRobot is building next generation predictive analytics software to make data scientists more productive, and scikit-learn is an integral part of our system. The variety of machine learning techniques in combination with the solid implementations that scikit-learn offers makes it a one-stop-shopping library for machine learning in Python. Moreover, its consistent API, well-tested code and permissive licensing allow us to use it in a production environment. Scikit-learn has literally saved us years of work we would have had to do ourselves to bring our product to market.
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Jeremy Achin, CEO & Co-founder DataRobot Inc.
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:target: https://www.datarobot.com
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`OkCupid `_
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We're using scikit-learn at OkCupid to evaluate and improve our matchmaking
system. The range of features it has, especially preprocessing utilities, means
we can use it for a wide variety of projects, and it's performant enough to
handle the volume of data that we need to sort through. The documentation is
really thorough, as well, which makes the library quite easy to use.
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David Koh - Senior Data Scientist at OkCupid
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:target: https://www.okcupid.com
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`Lovely `_
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At Lovely, we strive to deliver the best apartment marketplace, with respect to
our users and our listings. From understanding user behavior, improving data
quality, and detecting fraud, scikit-learn is a regular tool for gathering
insights, predictive modeling and improving our product. The easy-to-read
documentation and intuitive architecture of the API makes machine learning both
explorable and accessible to a wide range of python developers. I'm constantly
recommending that more developers and scientists try scikit-learn.
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Simon Frid - Data Scientist, Lead at Lovely
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:target: https://livelovely.com
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`Data Publica `_
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Data Publica builds a new predictive sales tool for commercial and marketing teams called C-Radar.
We extensively use scikit-learn to build segmentations of customers through clustering, and to predict future customers based on past partnerships success or failure.
We also categorize companies using their website communication thanks to scikit-learn and its machine learning algorithm implementations.
Eventually, machine learning makes it possible to detect weak signals that traditional tools cannot see.
All these complex tasks are performed in an easy and straightforward way thanks to the great quality of the scikit-learn framework.
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Guillaume Lebourgeois & Samuel Charron - Data Scientists at Data Publica
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:target: http://www.data-publica.com/
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`Machinalis `_
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Scikit-learn is the cornerstone of all the machine learning projects carried at
Machinalis. It has a consistent API, a wide selection of algorithms and lots
of auxiliary tools to deal with the boilerplate.
We have used it in production environments on a variety of projects
including click-through rate prediction, `information extraction `_,
and even counting sheep!
In fact, we use it so much that we've started to freeze our common use cases
into Python packages, some of them open-sourced, like
`FeatureForge `_ .
Scikit-learn in one word: Awesome.
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Rafael Carrascosa, Lead developer
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:target: https://www.machinalis.com/
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`solido `_
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Scikit-learn is helping to drive Moore's Law, via Solido. Solido creates
computer-aided design tools used by the majority of top-20 semiconductor
companies and fabs, to design the bleeding-edge chips inside smartphones,
automobiles, and more. Scikit-learn helps to power Solido's algorithms for
rare-event estimation, worst-case verification, optimization, and more. At
Solido, we are particularly fond of scikit-learn's libraries for Gaussian
Process models, large-scale regularized linear regression, and classification.
Scikit-learn has increased our productivity, because for many ML problems we no
longer need to “roll our own” code. `This PyData 2014 talk `_ has details.
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Trent McConaghy, founder, Solido Design Automation Inc.
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:target: https://www.solidodesign.com/
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`INFONEA `_
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We employ scikit-learn for rapid prototyping and custom-made Data Science
solutions within our in-memory based Business Intelligence Software
INFONEA®. As a well-documented and comprehensive collection of
state-of-the-art algorithms and pipelining methods, scikit-learn enables
us to provide flexible and scalable scientific analysis solutions. Thus,
scikit-learn is immensely valuable in realizing a powerful integration of
Data Science technology within self-service business analytics.
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Thorsten Kranz, Data Scientist, Coma Soft AG.
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:target: http://www.infonea.com/en/
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`Dataiku `_
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Our software, Data Science Studio (DSS), enables users to create data services
that combine `ETL `_ with
Machine Learning. Our Machine Learning module integrates
many scikit-learn algorithms. The scikit-learn library is a perfect integration
with DSS because it offers algorithms for virtually all business cases. Our goal
is to offer a transparent and flexible tool that makes it easier to optimize
time consuming aspects of building a data service, preparing data, and training
machine learning algorithms on all types of data.
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Florian Douetteau, CEO, Dataiku
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:target: https://www.dataiku.com/
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`Otto Group `_
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Here at Otto Group, one of global Big Five B2C online retailers, we are using
scikit-learn in all aspects of our daily work from data exploration to development
of machine learning application to the productive deployment of those services.
It helps us to tackle machine learning problems ranging from e-commerce to logistics.
It consistent APIs enabled us to build the `Palladium REST-API framework
`_ around it and continuously deliver
scikit-learn based services.
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Christian Rammig, Head of Data Science, Otto Group
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.. image:: images/ottogroup_logo.png
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:target: https://ottogroup.com
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`Zopa `_
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At Zopa, the first ever Peer-to-Peer lending platform, we extensively use scikit-learn
to run the business and optimize our users' experience. It powers our
Machine Learning models involved in credit risk, fraud risk, marketing, and pricing,
and has been used for originating at least 1 billion GBP worth of Zopa loans.
It is very well documented, powerful, and simple to use. We are grateful for the
capabilities it has provided, and for allowing us to deliver on our mission of making
money simple and fair.
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Vlasios Vasileiou, Head of Data Science, Zopa
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:target: https://zopa.com
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`MARS `_
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Scikit-Learn is integral to the Machine Learning Ecosystem at Mars. Whether
we're designing better recipes for petfood or closely analysing our cocoa
supply chain, Scikit-Learn is used as a tool for rapidly prototyping ideas
and taking them to production. This allows us to better understand and meet
the needs of our consumers worldwide. Scikit-Learn's feature-rich toolset is
easy to use and equips our associates with the capabilities they need to
solve the business challenges they face every day.
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Michael Fitzke Next Generation Technologies Sr Leader, Mars Inc.
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.. image:: images/mars.png
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:target: https://www.mars.com/global
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`BNP Paribas Cardif `_
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BNP Paribas Cardif uses scikit-learn for several of its machine learning models
in production. Our internal community of developers and data scientists has
been using scikit-learn since 2015, for several reasons: the quality of the
developments, documentation and contribution governance, and the sheer size of
the contributing community. We even explicitly mention the use of
scikit-learn's pipelines in our internal model risk governance as one of our
good practices to decrease operational risks and overfitting risk. As a way to
support open source software development and in particular scikit-learn
project, we decided to participate to scikit-learn's consortium at La Fondation
Inria since its creation in 2018.
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Sébastien Conort, Chief Data Scientist, BNP Paribas Cardif
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.. image:: images/bnp_paribas_cardif.png
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:target: https://www.bnpparibascardif.com/
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