Based on my prior knowledge, I will analyze the relationship between the feature "pdays" and the task of whether the client subscribes to a term deposit or not.

Analyzing the feature "pdays":

- "pdays" represents the number of days that passed by after the client was last contacted from a previous campaign.
- If the value of "pdays" is 999, it indicates that the client was not previously contacted.
- Clients who were not previously contacted (pdays = 999) may have a higher likelihood of subscribing to a term deposit as they may be more receptive to the current campaign.
- However, if a client was previously contacted and had a lower value for "pdays", it may indicate that they were recently contacted and their decision to subscribe to a term deposit may be influenced by that previous contact.

Based on this analysis, I will create a dictionary with possible values of "pdays" for each target class:

```json
{
	"no": [999, 999, 999, 999, 999],
	"yes": [1.0, 1.0, 2.0, 1.0, 2.0]
}
```
Please note that these values are just examples and not necessarily the exact values observed in the dataset. The values for "pdays" are presented as floats in the list.