MindsDB VS scikit-learn

I represent a community-driven open source project called MindsDB (see on GitHub). We need your feedback about our concept for doing machine learning using SQL! It is called AI Tables and aims to democratize machine learning for all who work with data. There's an article on Medium with SQL commands examples.

AI Tables is a part of GitHub project by MindsDB and are available as open-source or as a managed cloud service. They integrate with traditional SQL and NoSQL databases and data streams like Kafka & Redis.

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I say 'usually' because it depends on what you're referring to as 'coding'. From what you're describing, it seems that you want to be able to take data, clean it up and perform a whole bunch of analysis/inferences on it. In that case, I think the coding skill there would be stuff that allows you to do data manipulation and data clean up (knowledge of R, knowing Python as it pertains to data stuff e.g. scikit learning). Knowing how to build an App would not necessarily be a selling skill

Perhaps this would be of interest to you: NOCATS

Are you using scikit-learn for your training? If so, you may try running the models on one another. If you're using custom kernels, you may want to use a different set of them for the test set.

Scikit-learn

In short, you can read through the code, it's open-source. For instance, you can find LogisticRegression here.

http://scikit-learn.org/ - Machine learning with Python https://www.tensorflow.org/ - Deep learning with Python https://www.djangoproject.com/ - https://www.python.org/dev/peps/pep-0008

My only gripe is that the Labs are in R and not Python, but honestly the [scikit-learn](https://scikit-learn.org/) user guide & docs have been straightforward enough to apply the same knowledge in Python for me with some trial and error.

I'd say, the first question you'd need to ask yourself is "Why do I want to do statistical tests" and "what kind of statistical tests do I want to do?". Most of them rely on a bunch of assumptions and just winging it will produce a number that will be reported and used but is terribly wrong. Funnily enough, scikit-learn does not directly give you p-values for this very reason and advise you to run the same regression in statsmodels.

Machine learning and statistical analysis? http://scikit-learn.org