Based on prior knowledge, we can analyze the relationship between the campaign feature and the target variable of whether the client subscribes to a term deposit.

The campaign feature represents the number of contacts performed during a campaign for a specific client. We can assume that a higher number of contacts during a campaign may indicate a higher level of engagement or interest from the client. 

By analyzing the data, we can create a dictionary with the appropriate ranges of values for the campaign feature for each target class ('no' and 'yes').

```json
{
	"no": [1.0, 2.0, 3.0, 4.0, 5.0],  
	"yes": [1.0, 2.0, 3.0, 4.0, 5.0]  
}
```

In this case, we have assumed that typical campaign values for both target classes include 1, 2, 3, 4, and 5. These values represent a range of contacts performed during a campaign, allowing us to understand their relationship with the likelihood of a client subscribing to a term deposit.

Please note that the specific values mentioned in the dictionary are just placeholders and should be replaced with the actual values obtained from the dataset during the analysis phase.