Continuing analysis from last year: Top 20 Python Machine Learning Open Source Projects, this year KDnuggets bring you latest top 20 Python Machine Learning Open Source Projects on Github. Strangely, some of the most active projects of last year have become stagnant and also some lost their position from top 20 (considering contributions and commits), whereas new 13 projects have entered into top 20. We can see in the following chart that PyMVPA has highest contribution rate compare to all top projects in the list. Surprisingly, Scikit-learn has low contribution rate, despite maximum no. of contributors compare to other projects. Reason behind this could be that, as PyMVPA is a new project and going through early phases of development, leading to many commits because of new ideas/features development, defect fixing, refactoring etc. Whereas, Scikit-learn is old and stable project leading to less no. of improvements or defect fixing.

PyMVPA is a Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. It is designed to integrate well with related software packages, such as scikit-learn, shogun, MDP, etc. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is free software and requires nothing but free-software to run. PyMVPA stands for MultiVariate Pattern Analysis (MVPA) in Python.