Michael CP

Deployed Attrition Model:

• A predictive attrition model was built in R and deployed with Shiny.  IBM Attrition data was sourced from Kaggle.

• Model tab - the end user can run the model on different departments to determine variable importance, review model performance, and compare results against predictions (click on the results icon on the bottom).

  • Use Case: As a strategic HR leader, you're positioned to delve into the intricacies of attrition across various departments. This model allows you to pinpoint key factors influencing attrition. By accessing the Results tab, you can juxtapose predicted outcomes with actual data, offering a clearer understanding of retention drivers.​

• Predict tab - the end user can enter different variable values to make a prediction on how long the employee is likely to stay with the company.

  • Use Case: For managers keen on retaining top talent, this section is invaluable. It enables you to simulate scenarios to gauge the impact of salary adjustments, promotions, or inclusion in talent programs on high performers' likelihood to stay. Discover proactive measures to ensure your key players remain engaged and committed.​​

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