To analyze the relationship between the feature "pdays" and the task of whether the client subscribes to a term deposit, we can make some assumptions based on prior knowledge:
- It is likely that if the number of days since the client was last contacted is low, the client is more likely to subscribe to a term deposit.
- Conversely, if the number of days since the client was last contacted is high, the client is less likely to subscribe to a term deposit.

Based on these assumptions, we can divide the possible values of "pdays" into different ranges:
- "pdays" values close to 0 could indicate recent contact with the client.
- "pdays" values between 1 and 7 could indicate contact within the past week.
- "pdays" values between 8 and 30 could indicate contact within the last month.
- "pdays" values between 31 and 90 could indicate contact within the last three months.
- "pdays" values greater than 90 could indicate contact more than three months ago.

By examining the data, we can create a dictionary where the values of "pdays" for each target class are represented as lists:

```json
{
	"no": [400.0, 551.0, 555.0, 556.0, 557.0],
	"yes": [1.0, 2.0, 3.0, 4.0, 5.0]
}
```
Note that the values shown above are just placeholder examples. The actual values will depend on the dataset being analyzed.