This project was developed within the CESMA Master’s program in collaboration with TIM. Instead of framing the problem as a standard classification task, the system was designed as a learning-to-rank pipeline for next-best-action recommendation.
That shift in framing matters because ranking is closer to the actual business decision: not just whether an action is good or bad, but which action should come first for a given user.
The pipeline combines careful validation, Bayesian optimization, and ensemble ranking strategies. The end result is a substantial improvement over baseline performance on NDCG@5, making the project a solid example of applied machine learning under realistic evaluation constraints.
Performance summary:
| Stage | NDCG@5 Score | Improvement vs Baseline |
|---|---|---|
| Baseline Model | 0.5030 | — |
| Best Single Model | 0.6838 | +35.94% |
| Best Ensemble | 0.6852 | +36.23% |
Overall, it is one of the clearest examples in the portfolio of taking a familiar ML task and reformulating it in a way that is better aligned with the actual decision problem.