We formulate the problem as a **supervised classification task**:

### Objective
 Build a machine learning model that predicts whether a given tender will result in (sufficient) savings, based on information known *before* the tender is awarded.

### Scope
The prediction model should be applicable across:
- multiple countries,  
- various procurement categories (e.g., CPV codes),  
- different economic environments and bidder behaviours.

The model should remain robust and generalizable, not tailored to a single country or a specific procurement sector.

### Limitations
- The underlying dataset may be large, heterogeneous, and contain missing or inconsistent values.  
- Data formats and structures may differ across country sources.  
- Economic and regulatory differences make the prediction problem non-trivial and require careful preprocessing.  
- The final model should generalize across contexts rather than overfit to a narrow subset of the data.
- The dataset is historical and may not fully capture future policy or market changes.
- The model is intended for **educational and research purposes**, not for fully automated decision-making.