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04_​Interactive_​Scatter_​Plot_​Visualisation_​with_​Python_​View_​node_​using_​Plotly_​package

Interactive Scatter Plot Visualisation with Python View node using Plotly package

This workflow is built to showcase the usage of Python View node that can be used to generate interactive view. The workflow trains a lasso regression to predict the housing prices. Further we use the Python View node to visualise the effect of features on price of the house using intercative scatter plot

Configure the Conda Environment Propogation node toexport the selected environmentThe Conda Environment Propaation node propgates the selectedenvironment witgh required python packages to the users system, so thatthe Python Script and Python View node can be excecuted succesfully Find below the steps to configureStep 1: Drag the Conda Propagation node to KNIME Analytics PlatformStep 2: Open the dialog and select the environment under the option"Conda Environment"Step 3: Include the python packages required for this workflow OR simplyclick "Include only explicitly installed" optionStep 4: Specify the "output variable name" and press "Ok" Configure the Python View node for using Plotly packagefor Intercative VisualisationThe Python View node can be provide with input table or object and it cangenerate view or an output image based on the user's selection of portsStep 1: Drag the Python View node to KNIME Analytics PlatformStep 2: Open the dialog and select the "Executble Selection" tab andprovide the flow variable for the conda environment coming from condaenvironment propagation node Step 3: Write your script in the "Script" tab and press "execute script" tounderstand if the execution was successful. partition intrain and validationdatasets Fit a sklearnLasso modelwith the selectedregularizationstrengthPredict onvalidationdataCaliforniaHosuing DatasetHouse AgeCategoryPartitioning Python Script Python Script Table Reader Conda EnvironmentPropagation Row Sampling Python View Rule Engine Configure the Conda Environment Propogation node toexport the selected environmentThe Conda Environment Propaation node propgates the selectedenvironment witgh required python packages to the users system, so thatthe Python Script and Python View node can be excecuted succesfully Find below the steps to configureStep 1: Drag the Conda Propagation node to KNIME Analytics PlatformStep 2: Open the dialog and select the environment under the option"Conda Environment"Step 3: Include the python packages required for this workflow OR simplyclick "Include only explicitly installed" optionStep 4: Specify the "output variable name" and press "Ok" Configure the Python View node for using Plotly packagefor Intercative VisualisationThe Python View node can be provide with input table or object and it cangenerate view or an output image based on the user's selection of portsStep 1: Drag the Python View node to KNIME Analytics PlatformStep 2: Open the dialog and select the "Executble Selection" tab andprovide the flow variable for the conda environment coming from condaenvironment propagation node Step 3: Write your script in the "Script" tab and press "execute script" tounderstand if the execution was successful. partition intrain and validationdatasets Fit a sklearnLasso modelwith the selectedregularizationstrengthPredict onvalidationdataCaliforniaHosuing DatasetHouse AgeCategoryPartitioning Python Script Python Script Table Reader Conda EnvironmentPropagation Row Sampling Python View Rule Engine

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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: Mahantesh Pattadkal
Created at: 2022-12-08
On NodePit since: 2023-01-12
Last update: 2024-05-01
Created with KNIME version: v4.7.0
Tags: PythonPython ViewInteractive VisualisationPlotlyVisualisation

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