Employee turnover can be a real headache for businesses, costing them not only in recruitment and training but also in lost productivity, missed opportunities, and reduced team morale. It’s not just about replacing someone who leaves—it’s about managing the ripple effects on the organization.
This project builds on my earlier work in workforce analytics. Last time, I tackled employee turnover using Python and a decision tree classifier. Now, I’m shifting gears with a new dataset, a different approach, and R as the tool of choice to dig deeper into understanding and predicting why employees leave.
- Turnover Insights: Calculate turnover rates and analyze them from multiple angles.
- Data Integration: Identify employee segments and combine data from different HR systems for more actionable insights.
- Feature Engineering: Create meaningful variables and demonstrate how information value (IV) can enhance analysis.
- Logistic Regression Modeling: Develop a predictive model to assess attrition while managing variable multicollinearity.
- Model Evaluation: Test the model’s accuracy and classify employees into risk categories.Evaluate the accuracy of the model and categorize employees into specific risk buckets.
- Strategic Planning: Design a targeted intervention strategy and estimate its ROI to show the business impact.