Includes a big library in order for you to be able to implement what you need in your algorithm, makes good use for what you need to complete your task at hand. Techniques are very developed.

Like other object based languages Python offers an initiative User experience

the python-recsys Library (https://github.com/ocelma/python-recsys) offer us the opportunity to evaluate libraries in the field of machine learning for python, to test the technological bases for building recommendation systems. The solution makes use of the python libraries: python-scipy, python-numpy, csc-pysparse, networkx, divisi2. The solution offers recommendations and predictions to the users of a system, through the transformation of input data, based on reactions and transactions of the users and their relationship with the components of the products with which they interact. It makes use of the SVD (singular-value decomposition) functionality to apply a factorization process of the user's valuation data entry matrix. This solution can be used for the construction of systems that need to predict product recommendations, to its users, when there is a high number of products and users, efficiently taking advantage of the transactions and interactions of users and products. It is a good tool to learn about machine learning systems, making use of statistical algorithms and innovative development techniques, very well constructed, with some of the best programming languages that exist: python. Very good library. highly recommended its use and implementation