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Crab

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10 reviews
  • 1 profiles
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Average star rating
4.6
Serving customers since
2012

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Crab Reviews

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HK
Haru K.
Software Developer at ML Hub
10/04/2018
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Verified Current User
Review source: G2 invite
Incentivized Review

Recommender System Builder For Everyone

It is one of the best available open source customized recommender engine builder in python. There are many points to mention from easy API to robust behavior but I really like one feature about it, the pre-loaded datasets. It might not sound to everyone that I am calling it really good feature but if you realise these helps one go hands-on without any delay. One can just pip install the library and start playing. These very much helps in learning phase.
AM
Anushka M.
Project Manager at Easesolutions Pvt. Ltd.
09/26/2018
Validated Reviewer
Review source: G2 invite
Incentivized Review

Build Customized Recommender Engines Faster With Python-Crab

The API is high level and one need not to have in-detail knowledge of 'how recommender engines work?' but basic knowledge of python will do. Also, they provide sample data sets where one can have hands on experience while learning.
TG
Tarang G.
Software Development Specialist at ML Hub
08/02/2018
Validated Reviewer
Verified Current User
Review source: G2 invite
Incentivized Review

Crab - A Python Recommender Engine Framework

Crab is a open source customized recommender engine creator which helps one to build recommender engines on the go without any high level knowledge of recommender engines. A recommender engine can be built within few steps - 1. Load a dataset 2. Build a model using the Crab API 3. Define a similarity metric. 4. Add a recommender over it, using the Crab API and you are done! It has high level API and also support for well known python libraries which makes it unique and easy to use.

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What is Crab?

Crab (https://muricoca.github.io) is a cutting-edge machine learning library designed to simplify the process of implementing complex recommendation algorithms. The platform prioritizes user-friendly interfaces and efficient data handling, making the use of collaborative filtering and recommender systems more accessible for both beginners and experienced data scientists. Crab's focus on modularity allows developers to easily customize and extend its features, facilitating innovative approaches to personalized recommendation tasks in diverse application areas from e-commerce to media streaming services.

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Year Founded
2012