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Speed_​dating

Topic Detection Analysis - Speed DatingTopic detection extracts relevant information elements from unstructured text documents and groups them to define a number of topics. This workflowillustrates how to perform a topic detection analysis on movie reviews.Task. Perform a topic detection in IMDb reviews. Data ReadingRead Speed Dating data from a CSV file. Data Cleansing - Classic Data Cleansing of documents: Remove columbs with partners that like to watch tvsportsRemove rows with missing values in clubbing columnRemove rows with values between 0-2 in the Preferenceof Attraction ColumnDouble-click the metanode to see the subworkflow Data Manipulation GroupBy:Count of qualities wantedby age, race, gender,met and matchedPivoting:age and group bymet and matchby the qualities wantedin a partner VisualizationColor ManagerTo set colors by raceData ExplorerInteractive view ofpartner preference Data Science ModelsDecision Tree Read Speed Dating Remove Column manuallyWatching tv sportsRemove RowsMissing:Interest in clubbingRemove Rowswith 0 or 1 inPref of AttractionRemove Rowswith 2 inPref of attractionCount of qualitieswantedby age, race, gender,met and matchedage and groupbymet and matchby the qualities wantedin a partnerNode 10Set colorsby raceInteractive view ofpartner preferencesNode 13Percent of preferences aboutpartnerby race70 Training 30 TestStratified sampling:Travel columnCSV Reader Column Filter Missing ValueColumn Filter Row Filter Rule-basedRow Filter GroupBy Pivoting Pie Chart(JFreeChart) Color Manager Data Explorer DecisionTree Learner Decision TreePredictor Partitioning Topic Detection Analysis - Speed DatingTopic detection extracts relevant information elements from unstructured text documents and groups them to define a number of topics. This workflowillustrates how to perform a topic detection analysis on movie reviews.Task. Perform a topic detection in IMDb reviews. Data ReadingRead Speed Dating data from a CSV file. Data Cleansing - Classic Data Cleansing of documents: Remove columbs with partners that like to watch tvsportsRemove rows with missing values in clubbing columnRemove rows with values between 0-2 in the Preferenceof Attraction ColumnDouble-click the metanode to see the subworkflow Data Manipulation GroupBy:Count of qualities wantedby age, race, gender,met and matchedPivoting:age and group bymet and matchby the qualities wantedin a partner VisualizationColor ManagerTo set colors by raceData ExplorerInteractive view ofpartner preference Data Science ModelsDecision Tree Read Speed Dating Remove Column manuallyWatching tv sportsRemove RowsMissing:Interest in clubbingRemove Rowswith 0 or 1 inPref of AttractionRemove Rowswith 2 inPref of attractionCount of qualitieswantedby age, race, gender,met and matchedage and groupbymet and matchby the qualities wantedin a partnerNode 10Set colorsby raceInteractive view ofpartner preferencesNode 13Percent of preferences aboutpartnerby race70 Training 30 TestStratified sampling:Travel columnCSV Reader Column Filter Missing ValueColumn Filter Row Filter Rule-basedRow Filter GroupBy Pivoting Pie Chart(JFreeChart) Color Manager Data Explorer DecisionTree Learner Decision TreePredictor Partitioning

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Created by: blura
Created at: 2022-04-22
On NodePit since: 2022-04-23
Last update: 2024-05-01
Created with KNIME version: v4.5.1

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