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-revisi…. It also requires installation of the R package "e1071".
TAGS: Open source, R, Python, H2O.ai
EXAMPLES Server: 50_Applications/28_Predicting_Departure_Delays/03_OpenSourceVizAndModeling50_Applications/28_Predicting_Departure_Delays/03_OpenSourceVizAndModeling*
Download a zip-archive
* Find more about the Examples Server here.
The link will open the workflow directly in KNIME Analytics Platform (requirements: Windows; KNIME Analytics Platform must be installed with the Installer version 3.2.0 or higher). In other cases, please use the link to a zip-archive or open the provided path manually