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

### Objective
To develop a machine learning model capable of predicting the lowest final bidding price in a public procurement tender using historical observations.

### 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.