Based on prior knowledge, we can analyze the relationship between the "pdays" feature and the target variable which is whether the client subscribes to a term deposit.

The "pdays" feature represents the number of days that passed by after the client was last contacted from a previous campaign. In the context of this task, we can assume that a higher value of "pdays" indicates a longer time since the last contact.

To analyze the relationship, we can look at the potential values of "pdays" for both the "yes" and "no" classes of the target variable.

Based on typical scenarios, we can assume that for the "no" class (client does not subscribe to a term deposit), the "pdays" value would usually be higher. This could imply that clients who were contacted a long time ago are less likely to subscribe.

On the other hand, for the "yes" class (client subscribes to a term deposit), the "pdays" value might be comparatively lower. This suggests that clients who were contacted more recently are more likely to subscribe.

Using this understanding, we can create a dictionary with the values as follows:

```json
{
	"no": [500.0, 550.0, 600.0, 650.0, 700.0],
	"yes": [5.0, 10.0, 15.0, 20.0, 25.0]
}
```

Please note that these values are just examples and not representative of the actual dataset. To obtain the accurate values, you would need to analyze the dataset specifically and determine the appropriate ranges of "pdays" values that represent the "no" and "yes" classes.