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Human Resource Attrition
Predictive Analytics Use Case
A company can determine which employees are most likely
to leave, and which ones are most likely to remain loyal. This
technique can be used to predict whether a particular
employee will leave and when it will happen and to
understand why particular employees leave.
Human Resource Attrition
Sample Application
Description
Target
Likelihood of employee attrition
Human Resource Attrition
Sample Application
• Job satisfaction
• Satisfaction with pay
• Performance-reward contingencies
• Past working experience
• Personal and demographic information about employees –
gender, age, education, income, marital status,
employment tenure
Influencing
Factors
Human Resource Attrition
Sample Application
Binary Logistic Regression is the method used for classifying
numeric and/or categorical data into two groups based on
predefined categories.
• Higher classification accuracy (>=70%) means the results
are reliable and accurate.
• Lower classification accuracy (<70%) means the model
needs to be rebuilt using different input parameters.
Algorithm(s)
Human Resource Attrition
Sample Application
Model
Visualisation
Human Resource Attrition
Sample Application
Model
Visualisation
Human Resource Attrition
Sample Application
Model
Visualisation
Human Resource Attrition
Sample Application
Model
Summary
Human Resource Attrition
Sample Application
Interpretation
Human Resource Attrition
Sample Application
Result
• Likelihood/probability of employee leaving the company.
• Flag containing ‘likely to leave’ and ’unlikely to leave’
information with ‘yes’ and ‘no’ values.
Human Resource Attrition
Sample Application
Result
Employee attrition prediction with probability value can be
carried out using APPLY functionality shown below
Human Resource Attrition
Sample Application
Result
Human Resource Attrition
Sample Application
Human Resource Attrition
Predictive Analytics Use Case
For more information, contact us today.
www.Smarten.com
contact@Smarten.com
Smarten – Human Resource Attrition Use Case - 2019

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