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Practicing Data Science
Internet of Things
Random forest
IoT Demand prediction NYC taxi datset Spark cluster Time series prediction Washington DC Bicycle
  1. Workflow
    Taxi demand prediction training workflow
    Demand prediction Random forest Time series prediction
    +5
    In this use case, we will use the NYC taxi dataset and a Random Forest to train a simple time series prediction model to predict …
    knime > Examples > 10_Big_Data > 02_Spark_Executor > 11_Taxi_Demand_Prediction > Training_workflow
    4
    knime
  2. Workflow
    Taxi demand prediction deployment
    Demand prediction Random forest Time series prediction
    +5
    In this use case, we will use the NYC taxi dataset and a Random Forest to train a simple time series prediction model to predict …
    knime > Examples > 10_Big_Data > 02_Spark_Executor > 11_Taxi_Demand_Prediction > Deployment_workflow
    2
    knime
  3. Workflow
    Lean Restocking Alert Signal: Training
    Backward feature elimination Random forest Decision tree
    +7
    This workflow implements an alarm system for bycicle restocking at Washington bike stations. The dataset is the bike dataset. Pro…
    knime > Examples > 50_Applications > 55_Bike_Restocking_Alert > 01_Bike_Restocking_Alert_Training_w_Feature_Selection
    1
    knime
  4. Workflow
    Taxi demand prediction training workflow
    Demand prediction Random forest Time series prediction
    +5
    knime > Codeless Time Series Analysis with KNIME > Chapter 13 > Multivariate_Training_one_output
    0
    knime
  5. Workflow
    Lean Restocking Alert Signal: Deployment
    Random forest Rule engine WebPortal
    +7
    This workflow deploys the lean restocking system for bycicles in Washington DC stations, as trained in another workflow. Data is …
    knime > Examples > 50_Applications > 55_Bike_Restocking_Alert > 02_Bike_Restocking_Alert_Deploy
    0
    knime
  6. Workflow
    Taxi demand prediction training workflow
    Demand prediction Random forest Time series prediction
    +5
    knime > Codeless Time Series Analysis with KNIME > Chapter 13 > Multivariate_Training_multiple_outputs
    0
    knime