Employee Turnover Prediction
Data-driven exploration of employee retention dynamics to predict turnover risk and identify key organizational drivers.
🏢 Business Context
High employee turnover leads to significant costs in recruitment, training, and lost productivity. Understanding why employees leave is crucial for HR departments to implement effective retention strategies.
📈 Analytical Approach
We analysed a dataset of employee demographics, performance metrics, and survey results. Using statistical correlation and regression models (and Machine Learning classifiers), we identified the strongest predictors of attrition.
- ✓Exploratory Data Analysis (EDA) to visualise trends.
- ✓Feature Engineering to create meaningful metrics.
- ✓Predictive modelling using Random Forest / Gradient Boosting.
Key Insights
The analysis revealed that lack of career progression and satisfaction with management were higher predictors of turnover than salary alone. The predictive model achieved 85% accuracy, allowing HR to proactively engage at-risk employees.
