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Employee burnout

Predict employee burnout at work

Employee burnout may occur for various reasons: employee under excessive pressure; bad relations with colleagues/seniors;not being given importance; unfair treatment;lack of specific role;lack of support etc. It is a simple workflow in HR Analytics demonstrating how to predict burn rate. Target, BurnRate is continuous. Dataset is from Kaggle (see link below). We also treat the problem as a classification problem after binning BurnRate into two classes.

Predicting employee burnoutDataSource: https://www.kaggle.com/datasets/blurredmachine/are-your-employees-burning-outLast amended: 22nd Jan, 2023My folder: C:\Users\Ashok\OneDrive\Documents\hr_analytics\employee_burnout Classification example Regression example filter outemp id &date of joiningRandom partition70:30R^2 of 0.808Visual Inspecttion:Relationshipbetween predicted and actual BurnRateBin BurnRate0-0.50.5-1.0removeBurnRatestratified70:3090%Date of Joiningextract: year,month, day of monremove rows withmissing burnoutvalues for othersuse 'median'emp burnout dataView:Statistics ViewNode 33Node 34Node 35Node 36 Column Filter Partitioning Numeric Scorer Scatter Plot(JFreeChart) Numeric Binner Column Filter Partitioning Scorer String to Date&Time Extract Date&TimeFields Missing Value Table Reader Statistics Random Forest Learner(Regression) Random Forest Predictor(Regression) Random ForestLearner Random ForestPredictor Predicting employee burnoutDataSource: https://www.kaggle.com/datasets/blurredmachine/are-your-employees-burning-outLast amended: 22nd Jan, 2023My folder: C:\Users\Ashok\OneDrive\Documents\hr_analytics\employee_burnout Classification example Regression example filter outemp id &date of joiningRandom partition70:30R^2 of 0.808Visual Inspecttion:Relationshipbetween predicted and actual BurnRateBin BurnRate0-0.50.5-1.0removeBurnRatestratified70:3090%Date of Joiningextract: year,month, day of monremove rows withmissing burnoutvalues for othersuse 'median'emp burnout dataView:Statistics ViewNode 33Node 34Node 35Node 36Column Filter Partitioning Numeric Scorer Scatter Plot(JFreeChart) Numeric Binner Column Filter Partitioning Scorer String to Date&Time Extract Date&TimeFields Missing Value Table Reader Statistics Random Forest Learner(Regression) Random Forest Predictor(Regression) Random ForestLearner Random ForestPredictor

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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: Ashok
Created at: 2023-01-09
On NodePit since: 2023-01-22
Last update: 2024-04-27
Created with KNIME version: v4.7.0
Tags: HR AnalyticsRegressionPeople Analytics

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