Icon

Churn prediction bhushan

Churn prediction
SECOND STEP: APPLYING A SAVED MODEL FIRST STEP: BUILDING THE MODEL CHURN PREDICTIONThis workflow trains a model to predict which employees will leave the company due to attrition. Having this information can help those who work in the HR department to identify improvement paths in their company and avoid the cost of attrition. After you execute the workflow, you can check the BIRT report to get some insights on the data.For more information see the workflow metadata. Find it here: View -> Description Read data Process data Data visualizations for reporting Partitioning Model training Choosing the model Reporting Train/Test partitionInput dataExclude columnsNode 27Node 34OversamplingNode 40Dataset with predictionsStatisticsPie chart imageNode 50GBT Confusion matrixGBT Accuracy statisticsMonthly income boxplotNode 57Pie chart after predictionsNode 74Node 75Monthly income bar chartNode 77Distance from home bar chartNode 87Node 88Node 89Scatter plotNode 91Node 92Leavers by departmentNode 95Roc curveNode 100Node 101Node 102Node 103Node 104Node 106Identify dummy variablesand missing valuesAge and Income binsTotalSatisfactionNode 112Node 113Node 114Node 115Input dataExclude columnsAge and Income binsTotalSatisfactionNode 122Node 126Node 127Node 128Node 129 Partitioning Excel Reader (XLS) Scorer Column Filter InteractiveHistogram (local) Scorer Statistics SMOTE LogisticRegression Learner Logistic RegressionPredictor Scorer CSV Writer Data to Report Image to Report Scorer Data to Report Data to Report Image to Report ConditionalBox Plot Image to Report Pie/Donut Chart Bar Chart Image to Report Bar Chart Image to Report Pie/Donut Chart Scatter Plot Color Manager Image to Report Bar Chart One to Many Image to Report ROC Curve Image to Report Reduce number ofunique values Gradient BoostedTrees Learner Random ForestLearner Random ForestPredictor Naive Bayes Learner Gradient BoostedTrees Predictor Statistics Numeric Binner Column Aggregator ROC Curve ROC Curve ROC Curve Joiner ROC Curve Model Writer Excel Reader (XLS) Column Filter Numeric Binner Column Aggregator Row Filter Random ForestPredictor Model Reader Naive BayesPredictor Wait... SECOND STEP: APPLYING A SAVED MODEL FIRST STEP: BUILDING THE MODEL CHURN PREDICTIONThis workflow trains a model to predict which employees will leave the company due to attrition. Having this information can help those who work in the HR department to identify improvement paths in their company and avoid the cost of attrition. After you execute the workflow, you can check the BIRT report to get some insights on the data.For more information see the workflow metadata. Find it here: View -> Description Read data Process data Data visualizations for reporting Partitioning Model training Choosing the model Reporting Train/Test partitionInput dataExclude columnsNode 27Node 34OversamplingNode 40Dataset with predictionsStatisticsPie chart imageNode 50GBT Confusion matrixGBT Accuracy statisticsMonthly income boxplotNode 57Pie chart after predictionsNode 74Node 75Monthly income bar chartNode 77Distance from home bar chartNode 87Node 88Node 89Scatter plotNode 91Node 92Leavers by departmentNode 95Roc curveNode 100Node 101Node 102Node 103Node 104Node 106Identify dummy variablesand missing valuesAge and Income binsTotalSatisfactionNode 112Node 113Node 114Node 115Input dataExclude columnsAge and Income binsTotalSatisfactionNode 122Node 126Node 127Node 128Node 129 Partitioning Excel Reader (XLS) Scorer Column Filter InteractiveHistogram (local) Scorer Statistics SMOTE LogisticRegression Learner Logistic RegressionPredictor Scorer CSV Writer Data to Report Image to Report Scorer Data to Report Data to Report Image to Report ConditionalBox Plot Image to Report Pie/Donut Chart Bar Chart Image to Report Bar Chart Image to Report Pie/Donut Chart Scatter Plot Color Manager Image to Report Bar Chart One to Many Image to Report ROC Curve Image to Report Reduce number ofunique values Gradient BoostedTrees Learner Random ForestLearner Random ForestPredictor Naive Bayes Learner Gradient BoostedTrees Predictor Statistics Numeric Binner Column Aggregator ROC Curve ROC Curve ROC Curve Joiner ROC Curve Model Writer Excel Reader (XLS) Column Filter Numeric Binner Column Aggregator Row Filter Random ForestPredictor Model Reader Naive BayesPredictor Wait...

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: Marc Guirao
Created at: 2018-05-18
On NodePit since: 2020-08-01
Last update: 2024-11-21
Created with KNIME version: v4.0.2
Tags: ClearPeaksemployee attritionBirt reportHRchurn predictor

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

Try NodePit Runner!