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

In general, a higher value of "pdays" indicates a longer time since the client was last contacted. Here's how the feature "pdays" may relate to the target variable:

1. If the client has not subscribed to a term deposit (target class "no"), the value of "pdays" could be within the following range:
   - 0 to 10: Some clients who were last contacted recently may still not subscribe.
   - 50 to 100: Clients who were not contacted for a long time may be less likely to subscribe.
   - 150 to 200: A longer interval between contacts may decrease the likelihood of subscription.
   - 300 to 400: Clients not contacted for a significant period are less likely to subscribe.
   - 500 to 1000: Clients who were not contacted for a very long period are even less likely to subscribe.

2. If the client has subscribed to a term deposit (target class "yes"), the value of "pdays" could be within the following range:
   - 0 to 10: Clients who were contacted recently may be more likely to subscribe.
   - 50 to 100: Recent contact may still have a positive impact on subscription.
   - 150 to 200: Clients who were contacted within a reasonable interval may still subscribe.
   - 300 to 400: Contacts made after some time may still result in subscriptions.
   - 500 to 1000: Even clients contacted after a long interval might subscribe.

Based on this analysis, the requested dictionary can be generated as follows in markdown code format:

```json
{
	"no": [0.0, 5.0, 80.0, 250.0, 700.0],
	"yes": [1.0, 7.0, 100.0, 400.0, 900.0]
}
```

Please note that the values provided in the dictionary are just approximate and can be adjusted based on the specific dataset and further analysis.