Based on prior knowledge, we can analyze the relationship between the feature "pdays" and the target variable "Does this client subscribe to a term deposit?". "pdays" represents the number of days that passed by after the client was last contacted from a previous campaign.

One hypothesis could be that if the client has been contacted more recently, they are more likely to subscribe to a term deposit. In this case, we would expect a lower value of "pdays" for clients who subscribed (target class "yes"). On the other hand, if the client has not been contacted for a long time, they might be less likely to subscribe, resulting in a higher value of "pdays" for clients who did not subscribe (target class "no").

Based on this analysis, we can create the following dictionary:

```json
{
	"no": [999.0, 998.0, 997.0, 996.0, 995.0],
	"yes": [1.0, 2.0, 3.0, 4.0, 5.0]
}
```

This dictionary shows 5 typical values for "pdays" for each target class. The float values are used to represent the number of days. Clients who did not subscribe (target "no") have values like 999.0, 998.0, etc., indicating a long period since their last contact. Clients who subscribed (target "yes") have values like 1.0, 2.0, etc., indicating a more recent contact.