Based on prior knowledge, the feature "pdays" represents the number of days that have passed since the client was last contacted from a previous campaign. In this case, the target variable is whether the client subscribed to a term deposit or not.

To analyze the relationship between the feature and the target, we can consider the following:
- If "pdays" is a high value, it means that a long time has passed since the last contact. This could potentially indicate that the client is less likely to subscribe to a term deposit.
- On the other hand, if "pdays" is a low value, it means that the client was contacted relatively recently. This might imply that the client is more likely to subscribe to a term deposit.

Using this knowledge, let's generate the dictionary:

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

In this example, we have included 5 typical "pdays" values for each target class. For the "no" class (client did not subscribe), we have chosen higher values indicating a significant time gap since the last contact. For the "yes" class (client subscribed), we have selected lower values representing a more recent contact. Please note that these specific values are arbitrary and may vary in real-world scenarios.