Icon

Final workflow

Data Preparation Caroz data preparion External Data Integration Model Evaluation Numeric Modeling Categorical Modelling: Decision Tree without numerical Cycle Time variable Model Evaluation Categorical Modelling: Decision Tree Model Evaluation Reading datafrom CAROZ Convert String to DoubleStatistical OverviewNode 5Node 6Correlations between features in the Caroz datasetInsert Month and Day of Week Columns Remove columnsthat contain morethan 50% missing valuesSet missing values:(1) string to "unknown"(2) number to the mean(3) type date&timeremove rows that have missing values Restrict to rows with DC 79Convert String to DateConvert String to TimeJoint Caroz and weather datasetsbased on last date columnWeather datasetConvert String to TimePartition the data into a training set (70%) and test set"Short" Cycle TimeLinear RegressionModelPredicted Cycle TImeSubset data ofactual and predicted cycle time (LR)Line Plot"Short" Cycle TIme Simple Regression TreeModel Predicted Cycle Time"Short" Cycle TimeRandom Forest ModelPredicted Cycle TimeCalculate accuracySubset data of actualand predicted cycle timeLine PlotStatistics summary of the model's predictionSubset data ofactual and predicted cycle time (LR)Line PlotStatistics summary ofthe model's predictionStatistics summary of the model'spredictionReplace Missing Values for the Booleans "Rejected","LZV", "Conveyor", "Fast Lane" and "Promo" as"False"Remove rows with cycle time between 0 and 5in Minutes columnCatogorize "Short" and"Long" Cycle time in Cycle Time (Categorical)String value "Rejected" to BooleanRemove rows with cycle time between 0 and 5in Categorical columnOnly include the Short bin rowsDT of Cycle Time (Categorical)use Gini index Predicted Cycle Time(Categorical)Partition the data into a training set (70%) and test setPredicted Cycle Time(Categorical)DT of Cycle Time (Categorical)uses Gain Ration measure Confusion matrixConfusion matrixPrimary School Holiday dataJoint another external data into the previous jointed datasetsbased on last date columnStatistics View of the whole datasetLinear Correlation between featuresCalculate accuracyCalculate accuracyRename the 2 Cycle TimeColumns Remove Cycle Time (Minutes)DT of Cycle Time (Categorical)use Gini index Predicted Cycle Time(Categorical)Partition the data into a training set (70%) and test setPredicted Cycle Time(Categorical)DT of Cycle Time (Categorical)uses Gain Ration measure Confusion matrixConfusion matrix Excel Reader String To Number Statistics Box Plot (local) ConditionalBox Plot Scatter Plot(local) Linear Correlation Extract Date&TimeFields Missing ValueColumn Filter Missing Value Row Filter String to Date&Time String to Date&Time Joiner Excel Reader String to Date&Time Partitioning Linear RegressionLearner RegressionPredictor Column Filter Line Plot (local) Simple RegressionTree Learner Simple RegressionTree Predictor Random Forest Learner(Regression) Random Forest Predictor(Regression) Scorer (deprecated) Column Filter Line Plot (local) Numeric Scorer Column Filter Line Plot (local) Numeric Scorer Numeric Scorer Missing Value Row Filter Numeric Binner String Manipulation Row Filter Row Filter DecisionTree Learner Decision TreePredictor Partitioning Decision TreePredictor DecisionTree Learner Scorer Scorer Excel Reader Joiner Statistics Linear Correlation Scorer (deprecated) Scorer (deprecated) Column Rename Decision Tree View Column Filter Decision Tree View Decision Tree View DecisionTree Learner Decision TreePredictor Partitioning Decision Tree View Decision TreePredictor DecisionTree Learner Scorer Scorer Data Preparation Caroz data preparion External Data Integration Model Evaluation Numeric Modeling Categorical Modelling: Decision Tree without numerical Cycle Time variable Model Evaluation Categorical Modelling: Decision Tree Model Evaluation Reading datafrom CAROZ Convert String to DoubleStatistical OverviewNode 5Node 6Correlations between features in the Caroz datasetInsert Month and Day of Week Columns Remove columnsthat contain morethan 50% missing valuesSet missing values:(1) string to "unknown"(2) number to the mean(3) type date&timeremove rows that have missing values Restrict to rows with DC 79Convert String to DateConvert String to TimeJoint Caroz and weather datasetsbased on last date columnWeather datasetConvert String to TimePartition the data into a training set (70%) and test set"Short" Cycle TimeLinear RegressionModelPredicted Cycle TImeSubset data ofactual and predicted cycle time (LR)Line Plot"Short" Cycle TIme Simple Regression TreeModel Predicted Cycle Time"Short" Cycle TimeRandom Forest ModelPredicted Cycle TimeCalculate accuracySubset data of actualand predicted cycle timeLine PlotStatistics summary of the model's predictionSubset data ofactual and predicted cycle time (LR)Line PlotStatistics summary ofthe model's predictionStatistics summary of the model'spredictionReplace Missing Values for the Booleans "Rejected","LZV", "Conveyor", "Fast Lane" and "Promo" as"False"Remove rows with cycle time between 0 and 5in Minutes columnCatogorize "Short" and"Long" Cycle time in Cycle Time (Categorical)String value "Rejected" to BooleanRemove rows with cycle time between 0 and 5in Categorical columnOnly include the Short bin rowsDT of Cycle Time (Categorical)use Gini index Predicted Cycle Time(Categorical)Partition the data into a training set (70%) and test setPredicted Cycle Time(Categorical)DT of Cycle Time (Categorical)uses Gain Ration measure Confusion matrixConfusion matrixPrimary School Holiday dataJoint another external data into the previous jointed datasetsbased on last date columnStatistics View of the whole datasetLinear Correlation between featuresCalculate accuracyCalculate accuracyRename the 2 Cycle TimeColumns Remove Cycle Time (Minutes)DT of Cycle Time (Categorical)use Gini index Predicted Cycle Time(Categorical)Partition the data into a training set (70%) and test setPredicted Cycle Time(Categorical)DT of Cycle Time (Categorical)uses Gain Ration measure Confusion matrixConfusion matrix Excel Reader String To Number Statistics Box Plot (local) ConditionalBox Plot Scatter Plot(local) Linear Correlation Extract Date&TimeFields Missing ValueColumn Filter Missing Value Row Filter String to Date&Time String to Date&Time Joiner Excel Reader String to Date&Time Partitioning Linear RegressionLearner RegressionPredictor Column Filter Line Plot (local) Simple RegressionTree Learner Simple RegressionTree Predictor Random Forest Learner(Regression) Random Forest Predictor(Regression) Scorer (deprecated) Column Filter Line Plot (local) Numeric Scorer Column Filter Line Plot (local) Numeric Scorer Numeric Scorer Missing Value Row Filter Numeric Binner String Manipulation Row Filter Row Filter DecisionTree Learner Decision TreePredictor Partitioning Decision TreePredictor DecisionTree Learner Scorer Scorer Excel Reader Joiner Statistics Linear Correlation Scorer (deprecated) Scorer (deprecated) Column Rename Decision Tree View Column Filter Decision Tree View Decision Tree View DecisionTree Learner Decision TreePredictor Partitioning Decision Tree View Decision TreePredictor DecisionTree Learner Scorer Scorer

Nodes

Extensions

Links

Download

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

Download Workflow
Created by: marienguyen
Created at: 2023-05-15
On NodePit since: 2023-05-26
Last update: 2024-04-27
Created with KNIME version: v4.7.3

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!