Predicting employee attrition and optimizing workforce retention with AI-driven insights.
View Code on GitHubHigh employee turnover was leading to increased recruitment costs and loss of institutional knowledge. HR teams lacked actionable data to identify at-risk employees and understand the underlying drivers of attrition.
Develop a comprehensive analytics platform to visualize workforce demographics, analyze compensation trends, and build a predictive model to identify employees with a high risk of leaving the organization.
Built an interactive dashboard using R Shiny. Implemented a Random Forest model for attrition prediction. Designed intuitive visualizations with Plotly and ggplot2 to uncover insights into salary, satisfaction, and work-life balance.
Delivered a tool that provides real-time visibility into workforce health. The predictive model successfully identifies at-risk employees, enabling proactive retention strategies and data-driven decision-making for HR leadership.
"One of the biggest takeaways was the importance of feature engineering. Creating a composite 'Risk Score' from multiple factors provided much more value to HR stakeholders than raw probabilities."
I also learned that dashboard performance is critical. Optimizing reactive expressions in Shiny significantly reduced load times for large datasets.