Hub
WorkflowWorkflow

Taxi Demand Prediction on Spark Training

Demand prediction Random forest Time series prediction Spark cluster NYC taxi datset
+2

Last edited: 

Drag & drop
Like
Download workflow
Copy short link
Workflow preview
This workflow uses the NYC taxi dataset and a Random Forest to train a simple time series prediction model to predict taxi demand in the next hour based on data from past hours. Given the large size of the dataset, we train and deploy the machine learning model on a Spark cluster. The KNIME Big Data Extension allows you to run a KNIME workflow on the big data platform you prefer, via in-database processing or via Spark.

Used extensions & nodes

Created with KNIME Analytics Platform version 4.4.2 Note: Not all extensions may be displayed.
  • KNIME Amazon Cloud Connectors Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.1

  • KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

  • KNIME Data Generation Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • KNIME Expressions Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • KNIME Extension for Apache Spark Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.4.1, 4.4.2

  • KNIME Extension for Local Big Data Environments Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

  • KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

  • KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • KNIME Plotly Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

  • KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

Discussion