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02 Taxi Demand Prediction on Spark Deployment

Taxi Demand Prediction on Spark Deployment
Deployment Workflow This workflow generates forecasts of taxi demand using a pre-trained Spark Random Forest model Dynamic Deployment Compare with actual values count trip_countper hour update timeto the next hourfor predictionview resultread the modelupdate timeto the next hourfor predictionSpark Lag Column Spark GroupBy Spark Sorter Preprocessing Update time Path to test set Table view Create Local BigData Environment Parquet to Spark Model Reader Spark Predictor(Regression) Spark to Table Table to Spark Spark Lag Column Spark Predictor(Regression) Spark to Table Spark to Table Update time Parquet to Spark Path to test set Spark to Table Column Appender Numeric Scorer Line Plot (Plotly) Column Rename Legacy Date&Timeto Date&Time RecursiveLoop Start Recursive Loop End Prepare input Prepare outputs Deployment Workflow This workflow generates forecasts of taxi demand using a pre-trained Spark Random Forest model Dynamic Deployment Compare with actual values count trip_countper hour update timeto the next hourfor predictionview resultread the modelupdate timeto the next hourfor predictionSpark Lag Column Spark GroupBy Spark Sorter Preprocessing Update time Path to test set Table view Create Local BigData Environment Parquet to Spark Model Reader Spark Predictor(Regression) Spark to Table Table to Spark Spark Lag Column Spark Predictor(Regression) Spark to Table Spark to Table Update time Parquet to Spark Path to test set Spark to Table Column Appender Numeric Scorer Line Plot (Plotly) Column Rename Legacy Date&Timeto Date&Time RecursiveLoop Start Recursive Loop End Prepare input Prepare outputs

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: dewi
Created at: 2019-02-07
On NodePit since: 2021-11-04
Last update: 2024-11-17
Created with KNIME version: v4.5.0
Tags: Demand predictionrandom foresttime series predictionSpark clusterNYC taxi datsetIoTInternet of Things

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