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Analytics - Model Selection to Predict Flight Departure Delays

Data science Machine learning Model selection Data preparation ETL
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This workflow trains a number of data analytics models and automatically selects the best model to predict departure delays from a selected airport. Data is the airline dataset downloadable from: http://stat-computing.org/dataexpo/2009/the-data.html. Departure delay is a delay > 15min. Default selected airport is ORD. This workflow implements data reading, ETL, outlier detection, dimensionality reduction, feature generation, feature selection, advanced data mining models, model selection and comparison. Data available in knime://knime.workflow/data/

External resources

  • KNIME Analytics: a Review
  • Advanced ETL Functionalities and Machine Learning Pre-Processing

Used extensions & nodes

Created with KNIME Analytics Platform version 4.5.0
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    KNIME Data Generation Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    KNIME Integrated Deployment Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    KNIME Statistics Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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