Based on my prior knowledge, I can analyze the relationship between the feature "day" and the task "Does this client subscribe to a term deposit?".

Generally, the last contact day of the month may have some influence on the client's decision to subscribe to a term deposit. Therefore, it is important to examine the range of values for the "day" feature that are associated with each target class.

Here is the dictionary with the possible values of the feature "day" for each target class:

```json
{
	"no": [1.0, 15.0, 20.0, 25.0, 30.0], 
	"yes": [5.0, 10.0, 15.0, 20.0, 25.0]
}
```

In the "no" target class, some typical day values could be 1, 15, 20, 25, and 30. Whereas in the "yes" target class, typical values could be 5, 10, 15, 20, and 25. Keep in mind that these values are just examples and may vary based on the specific dataset and context.