# Crab Reviews **Vendor:** Crab **Category:** [Machine Learning Software](https://www.g2.com/categories/machine-learning) **Average Rating:** 4.6/5.0 **Total Reviews:** 10 ## About Crab Crab as known as scikits.recommender is a Python framework for building recommender engines that integrate with the world of scientific Python packages (numpy, scipy, matplotlib), provide a rich set of components from which user can construct a customized recommender system from a set of algorithms and be usable in various contexts: \*\* science and engineering \*\* . ## Crab Reviews ### 1. Recommender System Builder For Everyone **Rating:** 4.5/5.0 stars **Reviewed by:** Haru K. | Software Developer, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** October 04, 2018 **What do you like best about Crab?** 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. **What do you dislike about Crab?** One thing I really don't like about Crab is the documentation. The documentation is never ever updated and actually the present documentation is really poor. The contributors to Crab should take the step and write a proper documentation. **Recommendations to others considering Crab:** I recommend not to rely on the original documentation as it is not maintained properly and don't have sufficient information. I better suggest to opt for different implementations available in your learning pipeline. Once you are used to API you can easily build your own recommender engine in no time. **What problems is Crab solving and how is that benefiting you?** Crab is meant to build recommender engines. At ML Hub we have number of clients that come with a definition to build customized recommender engines, for example: e-commerce websites, online book stores and many more. So we use Crab to develop such recommender engines. We rely on it because it has the easiest API and also there is no complex flow while building a recommender engine. It can be built in few lines of code (pre-processing not considered) ### 2. Crab - A Python Recommender Engine Framework **Rating:** 4.5/5.0 stars **Reviewed by:** Tarang G. | Software Development Specialist, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** August 02, 2018 **What do you like best about Crab?** 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. **What do you dislike about Crab?** This the only thing I prefer while building a recommender engine so I basically have no dislikes for Crab but I would like one thing to be improved, the documentation. **Recommendations to others considering Crab:** There are very few tutorials or videos available on internet for the Crab. Once I was wondering how a newbie would be able to learn this very helpful framework without a good source of learning and browsed the web for a while and came out with some very useful links that can for sure help you out. Here they go - http://muricoca.github.io/crab/tutorial.html https://www.analyticsvidhya.com/blog/2016/06/quick-guide-build-recommendation-engine-python/ https://www.youtube.com/watch?v=Xll2ZFic-Ak http://aimotion.blogspot.com/2011/05/crab-python-framework-for-building.html **What problems is Crab solving and how is that benefiting you?** ML Hub Team uses the open source quality tools and software available in the market to develop solutions to the real life problems in the domain of Artificial Intelligence, NLP and Computer Vision. As we come across various projects regarding recommender engines we solely depend on Crab to bring solution to it. Crab has proved itself over the years and has been very helpful throughout the journey with the high level and easy to learn API. ### 3. Build Recommender Engines On The Go Using Crab **Rating:** 4.5/5.0 stars **Reviewed by:** Prit T. | Software Development Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** July 27, 2018 **What do you like best about Crab?** - Crab is a open source which means it's freely available and anyone can raise a issue and request a feature or may implement it himself. This is one thing that captivates me towards it. - Also as it supports various Python libraries like pandas and numpy makes itself unique from other frameworks as there a lot of such implementations available that don't support this libraries. **What do you dislike about Crab?** Yes, it has many advantages but I have pointed out a major drawback. It supports on Pyhton which makes its use restricted to Python developers and all the windows are closed for developers using different languages like Java, C/C++, JavaScript and so on. I find this as a prime drawback as it becomes difficult to switch languages, makes us switch frameworks too. That increase burden of learning new library for new language. **Recommendations to others considering Crab:** Here is a good introductory tutorial for Crab - https://archive.org/details/Thursday-203-1-CrabARecommendationEngineFrameworkForPython . This will help one to understand the overall framework and how it can be used to build customised recommender engines. I highly recommend one to watch the full video. **What problems is Crab solving and how is that benefiting you?** ML Hub is a team concentrated to bring solutions of real life problems where we use current hot technologies like Recommender Engines. The Crab library provides us with a bunch of well tested and dependable algorithms which we can directly use to build customised recommender engines on the go. Using this library we were able to ease such process and increase our throughput. ### 4. The Best Recommender Engine Library For Python **Rating:** 4.5/5.0 stars **Reviewed by:** Alpesh S. | Project Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** July 27, 2018 **What do you like best about Crab?** - The very first benefit about this library is, it is open source. - It supports various famous python libraries used for data manipulation and visualisation like numpy, pandas and matplotlib in the core making developers tasks easier. - The API is easy to use and it's simple to understand the functionalities using the documentation provided by Crab team. **What do you dislike about Crab?** The documentations needs some improvement as there are many flaws like some API is not at all mentioned and if mentioned has very less description. Also there aren't much example available making it difficult for the new learners. They have to depend on other sources, which I think is major drawback of this library. **Recommendations to others considering Crab:** Installing Crab sometimes lead you to number of errors and is the thing that need to be taken care of. I faced some issues while installing it and then found this website where there was a neat explanation on the installation process and helped me to install it step by step. I recommend the people facing such issue to look for the following link - http://muricoca.github.io/crab/install.html **What problems is Crab solving and how is that benefiting you?** ML Hubs is a team whose aim is to solve complex problems using the technologies like Machine Learning, Deep Learning, Natural Language Processing and Computer Vision. So this library is included in the stack we use to handle recommendation related problems. We prefer Crab as it has highly efficient API and also is not that complex to understand and handle. ### 5. Build Customized Recommender Engines Faster With Python-Crab **Rating:** 4.5/5.0 stars **Reviewed by:** Anushka M. | Project Manager, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** September 26, 2018 **What do you like best about 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. **What do you dislike about Crab?** The worst thing about Crab is the documentation part. They have worked well to create such an awesome library, but have not given enough to create a good documentation. I think they should focus on this part and everything else is great! **Recommendations to others considering Crab:** Crab is easy to learn. Neither the API is complex nor it's usage. I recommend everyone should give it a try and the good thing is it won't take a lot of time of yours to learn it. One can learn in a weekend. Make a simple recommender engine, which I think will take 15 lines of python code at max and then try different things. In short, go give it a try! It's Awesome! **What problems is Crab solving and how is that benefiting you?** We at Easesolutions Pvt. Ltd. focus on providing best solutions to our clients. We have clients that have some e-commerce websites, some have websites of their own products, and so on. In such scenarios they ask to build customised recommender engines. We use crab to meet their needs as it is easy to use, but still gives the best results. ### 6. Create customised recommendation engines using Crab **Rating:** 4.5/5.0 stars **Reviewed by:** Shilpa M. | Product Manager, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2018 **What do you like best about Crab?** This library has state of art recommendation engine algorithms which are well optimised and ready to use at production level. Along with this I really like the ease with which I can work with this library as it supports well known python libraries like numpy and matplotlib. **What do you dislike about Crab?** The documentation needs some improvement as the API descriptions are not mentioned properly and it also lacks in examples. There are no enough examples which makes it difficult to learn and one need to spend more time in understanding than in implementing. **Recommendations to others considering Crab:** There are some major issues with this library so make sure you are aware of it before you start using it. Many users have found out these issues and posted on GitHub. You can have a look : https://github.com/muricoca/crab/issues **What problems is Crab solving and how is that benefiting you?** We at TechDynasty build customised Recommendation Engines as per the customer desires and domain of their interest. ### 7. Recommender Engines In Few Lines Of Code **Rating:** 4.5/5.0 stars **Reviewed by:** Varsha S. | CTO, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** July 31, 2018 **What do you like best about Crab?** This framework is well organized and has high level API which enables developer to code recommender engines within few lines of code without any prior knowledge of how recommender engines work . The recommender engines built are dynamic and reusable which means same model can be used to train a dynamic amount of data or even a different set of data with few tweaks. This framework supports libraries like numpy and pandas which makes data manipulation easier comparatively and boosts the process to some extent. **What do you dislike about Crab?** There is no proper documentation provided by the developer community of the project as well as very few examples are available. The community should take a step and improve on this points. This really hurdles a learner and are sincere issues to take care of. **Recommendations to others considering Crab:** Libraries like numpy and padas are a great boost to the whole process. As they are highly efficient data manipulation libraries the data cleaning and normalization process is eased and you can more focus on the main recommender engine algorithm. I advice people to learn these libraries. **What problems is Crab solving and how is that benefiting you?** Crab is framework to build customized recommender engines, we at ML Hub have such projects where we need to work on recommender engines for which we rely on Crab. As Crab has good support for various data manipulation libraries and also high level API makes its best for the purpose. Crab has saved tons of time for us. ### 8. Recommender Engine Framewrok **Rating:** 4.5/5.0 stars **Reviewed by:** Tushar D. | Senior Software Engineer, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** May 05, 2018 **What do you like best about Crab?** - It easily merge with the famous python libraries like numpy, pandas, e.t.c which makes work more easier. - One can directly implement algorithms like Collaborative Filtering on the go using the Crab API. - It also provides dataset loading utilities. **What do you dislike about Crab?** The documentation is not that well maintained, in some parts it lacks explanation and even there are no such examples which helps one learn it. **Recommendations to others considering Crab:** The documentation is not that well maintained better refer the GitHub repository of the same project https://github.com/muricoca/crab **What problems is Crab solving and how is that benefiting you?** Built Recommender Engine for several projects we have developed. ### 9. Building Recommender Systems made easy **Rating:** 4.5/5.0 stars **Reviewed by:** Mohit S. | SoftwaCre Engineer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** June 03, 2018 **What do you like best about Crab?** This library is one of its kind. It reduces all the stress of complex coding while one builds one's own customised recommender system by providing a simple and easy to use API. **What do you dislike about Crab?** It has support only for Python programming language and also the documentation is not that good. **Recommendations to others considering Crab:** The official documentation is the place to start but not a thing you should always refer as it is not up to date and doesn't have sufficient examples. You may find various GitHub repositories for the same, please prefer them instead of documentation. **What problems is Crab solving and how is that benefiting you?** Building customised recommender engines as per the requirement of clients. ### 10. Customize Recommender System Builder framework for python **Rating:** 5.0/5.0 stars **Reviewed by:** Rahul T. | Project Manager, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** October 22, 2017 **What do you like best about Crab?** - It's easy interaction with other helper libraries of python like numpy and matplotlib. - Documentation **What do you dislike about Crab?** No enough video tutorials available, even on youtube. The developers should work on the same to help the users. **Recommendations to others considering Crab:** Refer http://muricoca.github.io/crab/tutorial.html to start your journey in Crab. **What problems is Crab solving and how is that benefiting you?** Building customized Recommender Engines for projects we develop at RG Developers. ## Crab Discussions - [What is Crab used for?](https://www.g2.com/discussions/what-is-crab-used-for) - [View Crab pricing details and edition comparison](https://www.g2.com/products/crab/reviews?section=pricing&secure%5Bexpires_at%5D=2026-05-15+11%3A15%3A24+-0500&secure%5Bsession_id%5D=c768f769-b2ee-4127-971c-05cde1dd566d&secure%5Btoken%5D=57f5b1a073d4004eb2176597cf3ffac65f2013d55f66827b53bd5b27f408d0b6&format=llm_user) ## Crab Features **Integration - Machine Learning** - Integration **Learning - Machine Learning** - Training Data - Actionable Insights - Algorithm ## Top Crab Alternatives - [Automation Anywhere Agentic Process Automation](https://www.g2.com/products/automation-anywhere-agentic-process-automation/reviews) - 4.5/5.0 (4,009 reviews) - [Demandbase One](https://www.g2.com/products/demandbase-one/reviews) - 4.4/5.0 (1,891 reviews) - [Phrase Localization Platform](https://www.g2.com/products/phrase-localization-platform/reviews) - 4.5/5.0 (1,260 reviews)