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Auto ML receives raw(ish) data and generates a supervised learning model from it. That is, it automates feature engineering and model selection/tuning.

What open source alternatives currently exist that when given raw data and told which variable to target, and will produce an ML model autonomously?

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AutoSklearn, AutoViML, TPOT, H2O are good choices. They can do feature engineering, but I would suggest to build your own feature based on domain knowledge.

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You can check out my open source library Auto_ViML which needs just one line of code to build many different ML models from the simplest to the most complex all under your command. It also provides charts and graphs if you set verbose to the highest level (2). In addition, I have also open sourced my library for EDA called AutoViz which allows you to visualize any sized data set (as long as it can fit into a pandas data frame) with one line of code. Both are available at this GitHub. https://github.com/AutoViML/Auto_ViML

https://github.com/AutoViML/AutoViz

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