This workflow uses airport and meteorlogical data to predict airline delays. It uses several open source integrations to both create simple visualizations of the data, and build models for delay prediction. It also compares the results of the various models. Execution of this workflow requires the following KNIME extensions: *KNIME H2O Machine Learning Integration *KNIME Python Integration. It also requires a configuration of Python 3.5 with pandas, scikit-learn, and matplotlib packages installed. For more information on configuring Python with KNIME, see https://www.knime.com/blog/setting-up-the-knime-python-extension-revisited-for-python-30-and-20. It also requires installation of the R package "e1071".
This is a companion discussion topic for the original entry at https://kni.me/w/ScRlFhW2rrFDyGuO