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Step 2: Data UnderstandingThese are various nodes/combinations of nodes that we used to gather a better understanding of the data and relationships among attributes. Please note that some nodes were used for morethan one function, so not every node used is included. Step 3: Data PreparationWe executed the following nodes to correct quality issues within the data set. Step 4: Model building and EvaluationThe following nodes is used to view the best model to calculate our data Step 5: DeploymentThe following workflow utilizes the Random Forest Learner node which is also connected to the Test data. Step 5: Statistics for the prediction results Node 1Diversity based on EthnicityCoversion to % (int1rat)Conversion to % (int2rat)Round the decimal valueAvg. of interestAvg. of InterestRound the decimal valueClassification by GenderFilter for EnrollmentDiversity based on ResidencyClassificationb by Residencyoriginal statisticscorrecting missing valuesIdenify Outlierscorrecting inconsistent values in territorycorrecting outliersCoversion of Instate to Binarydiscretization of avg_incomecorrecting inconsistent values in territorycorrecting negative inconsistent value in intit_spanValidation for init_spanvalidation for outliersDrop attributesMailq vs enrollmentPartioning DataTest DataPrediction DataData StatsEnroll ROC CurvePartioning DataNormalize Dataapplying normalizing dataTest Data Prediction DataEnroll ROC CurveData StatsPartion DataTest DataPrediction DataEnroll ROC CurveData Statsdrop attributesConverting string to integerValidation for init_spancorrecting inconsistent values in territoryCoversion of Instate to BinaryDrop attributescorrecting inconsistent values in territoryNode 96correcting negative inconsistent value in intit_spandiscretization of avg_incomevalidation for outlierscorrecting missing valuescorrecting outliersPrediction DataNode 107Node 108Node 109InstateSexFilter enrolledFilter not enrolledAvg. IncomeEthnicityTerritoryAvg. IncomeSexInstateEthnicityTerritoryTotal ContactsCampus VisitMailQTelecqPremierFile Reader Pie/Donut Chart Math Formula Math Formula Round Double GroupBy GroupBy Round Double Pie/Donut Chart Row Filter Pie/Donut Chart Pie/Donut Chart Statistics Missing Value Box Plot Cell Replacer Numeric Outliers Rule Engine Numeric Binner Table Creator Numeric Outliers Box Plot Box Plot Column Filter ConditionalBox Plot Partitioning DecisionTree Learner Decision TreePredictor Scorer ROC Curve Partitioning Normalizer Normalizer (Apply) RProp MLP Learner MultiLayerPerceptronPredictor ROC Curve Scorer Partitioning Random ForestLearner Random ForestPredictor ROC Curve Scorer Column Filter One to Many Box Plot Table Creator Rule Engine Column Filter Cell Replacer Bar Chart Numeric Outliers Numeric Binner Box Plot Missing Value Numeric Outliers Random ForestPredictor File Reader Value Counter Interactive Piechart (local) Bar Chart Pie/Donut Chart Row Filter Row Filter Bar Chart Pie/Donut Chart Bar Chart Bar Chart Pie/Donut Chart Bar Chart Pie/Donut Chart Bar Chart Pivoting Pivoting Pivoting Pivoting Pivoting Step 2: Data UnderstandingThese are various nodes/combinations of nodes that we used to gather a better understanding of the data and relationships among attributes. Please note that some nodes were used for morethan one function, so not every node used is included. Step 3: Data PreparationWe executed the following nodes to correct quality issues within the data set. Step 4: Model building and EvaluationThe following nodes is used to view the best model to calculate our data Step 5: DeploymentThe following workflow utilizes the Random Forest Learner node which is also connected to the Test data. Step 5: Statistics for the prediction results Node 1Diversity based on EthnicityCoversion to % (int1rat)Conversion to % (int2rat)Round the decimal valueAvg. of interestAvg. of InterestRound the decimal valueClassification by GenderFilter for EnrollmentDiversity based on ResidencyClassificationb by Residencyoriginal statisticscorrecting missing valuesIdenify Outlierscorrecting inconsistent values in territorycorrecting outliersCoversion of Instate to Binarydiscretization of avg_incomecorrecting inconsistent values in territorycorrecting negative inconsistent value in intit_spanValidation for init_spanvalidation for outliersDrop attributesMailq vs enrollmentPartioning DataTest DataPrediction DataData StatsEnroll ROC CurvePartioning DataNormalize Dataapplying normalizing dataTest Data Prediction DataEnroll ROC CurveData StatsPartion DataTest DataPrediction DataEnroll ROC CurveData Statsdrop attributesConverting string to integerValidation for init_spancorrecting inconsistent values in territoryCoversion of Instate to BinaryDrop attributescorrecting inconsistent values in territoryNode 96correcting negative inconsistent value in intit_spandiscretization of avg_incomevalidation for outlierscorrecting missing valuescorrecting outliersPrediction DataNode 107Node 108Node 109InstateSexFilter enrolledFilter not enrolledAvg. IncomeEthnicityTerritoryAvg. IncomeSexInstateEthnicityTerritoryTotal ContactsCampus VisitMailQTelecqPremierFile Reader Pie/Donut Chart Math Formula Math Formula Round Double GroupBy GroupBy Round Double Pie/Donut Chart Row Filter Pie/Donut Chart Pie/Donut Chart Statistics Missing Value Box Plot Cell Replacer Numeric Outliers Rule Engine Numeric Binner Table Creator Numeric Outliers Box Plot Box Plot Column Filter ConditionalBox Plot Partitioning DecisionTree Learner Decision TreePredictor Scorer ROC Curve Partitioning Normalizer Normalizer (Apply) RProp MLP Learner MultiLayerPerceptronPredictor ROC Curve Scorer Partitioning Random ForestLearner Random ForestPredictor ROC Curve Scorer Column Filter One to Many Box Plot Table Creator Rule Engine Column Filter Cell Replacer Bar Chart Numeric Outliers Numeric Binner Box Plot Missing Value Numeric Outliers Random ForestPredictor File Reader Value Counter Interactive Piechart (local) Bar Chart Pie/Donut Chart Row Filter Row Filter Bar Chart Pie/Donut Chart Bar Chart Bar Chart Pie/Donut Chart Bar Chart Pie/Donut Chart Bar Chart Pivoting Pivoting Pivoting Pivoting Pivoting

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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: sramakr
Created at: 2021-09-25
On NodePit since: 2021-11-15
Last update: 2024-11-21
Created with KNIME version: v4.4.1

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