Räägi mulle töötajate lahkumise prognoosimise projektist: mida sa tegid, mis välja tuli, millega sa uhkust tunned?

In the employee turnover prediction project, I was involved in collecting and analyzing HR data, as well as building machine learning models to identify factors influencing departure. I used classification methods such as Random Forest and Gradient Boosting to predict the likelihood of resignation.

The result was an increase in prediction accuracy to 85%, which allowed the HR department to take timely measures to retain key employees. I am particularly proud of implementing interpretable models that helped explain the reasons for turnover and improve internal processes.