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07_​Customer_​prediction_​with_​H2O

Workflow

Customer prediction with H2O in KNIME
The purpose of this workflow is to showcase the ease of use of the H2O functionalities from within KNIME. As a real world usecase we chose the "Restaurant Visitor Forecasting" competition on Kaggle.com: https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting The workflow contains the following steps: - Data preparation: Reading, cleaning, joining data and feature creation - Creation of a local H2O context and transformation of a KNIME data table into an H2O frame - Modeling of three different models including cross validation and parameter optimization - Selection of the best model - Deployment: Converting the H2O model into an H2O MOJO and doing the prediction for the Kaggle competition Feel free to create some more features and try additional parameters in the optimization loop to improve your predictions. For legal reasons we are not allowed to ship the dataset from Kaggle with our workflow. To get access to the data you have to sign in to Kaggle and accept the conditions of participation for the competetion. Afterwards you can download the data, save it in the data folder of this KNIME project and run the workflow.
Customer prediction with H2O in KNIMEThe purpose of this workflow is to showcase the ease of use of the H2O functionalities from within KNIME. As a real world usecase we chose the "Restaurant VisitorForecasting" competition on Kaggle.com: https://www.kaggle.com/c/recruit-restaurant-visitor-forecastingThe workflow contains the following steps:- Data preparation: Reading, cleaning, joining data and feature creation- Creation of a local H2O context and transformation of a KNIME data table into an H2O frame- Modeling of three different models including cross validation and parameter optimization- Selection of the best model- Deployment: Converting the H2O model into an H2O MOJO and doing the prediction for the Kaggle competitionFeel free to create some more features and try additional parameters in the optimization loop to improve your predictions.For legal reasons we are not allowed to ship the dataset from Kaggle with our workflow. To get access to the data you have to sign in to Kaggle and accept the conditions ofparticipation for the competetion. Afterwards you can download the data, unzip it, adjust the path pointing to the files in the node dialog of the List Files node and run theworkflow. You can find the kaggle challenge here https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting. Native KNIME nodes KNIME H2O Extensions Native KNIME nodes Mix of Native KNIMEnodes and the KNIMEH2O Extensions KNIME H2O Extensions Native KNIME nodes Change path to folder with unzipped data H2O Local Context Table to H2O Data preparation GeneralizedLinear Model GradientBoosting Machine Random Forest H2O MOJO Predictor(Regression) H2O Model to MOJO H2O MOJO Writer List Files Change table into Kagglesubmission format Select best model Customer prediction with H2O in KNIMEThe purpose of this workflow is to showcase the ease of use of the H2O functionalities from within KNIME. As a real world usecase we chose the "Restaurant VisitorForecasting" competition on Kaggle.com: https://www.kaggle.com/c/recruit-restaurant-visitor-forecastingThe workflow contains the following steps:- Data preparation: Reading, cleaning, joining data and feature creation- Creation of a local H2O context and transformation of a KNIME data table into an H2O frame- Modeling of three different models including cross validation and parameter optimization- Selection of the best model- Deployment: Converting the H2O model into an H2O MOJO and doing the prediction for the Kaggle competitionFeel free to create some more features and try additional parameters in the optimization loop to improve your predictions.For legal reasons we are not allowed to ship the dataset from Kaggle with our workflow. To get access to the data you have to sign in to Kaggle and accept the conditions ofparticipation for the competetion. Afterwards you can download the data, unzip it, adjust the path pointing to the files in the node dialog of the List Files node and run theworkflow. You can find the kaggle challenge here https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting. Native KNIME nodes KNIME H2O Extensions Native KNIME nodes Mix of Native KNIMEnodes and the KNIMEH2O Extensions KNIME H2O Extensions Native KNIME nodes Change path to folder with unzipped data H2O Local Context Table to H2O Data preparation GeneralizedLinear Model GradientBoosting Machine Random Forest H2O MOJO Predictor(Regression) H2O Model to MOJO H2O MOJO Writer List Files Change table into Kagglesubmission format Select best model

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Nodes

07_​Customer_​prediction_​with_​H2O consists of the following 131 nodes(s):

Plugins

07_​Customer_​prediction_​with_​H2O contains nodes provided by the following 10 plugin(s):