Based on prior knowledge, we can analyze the relationship between the "day" feature and the task of whether the client subscribes to a term deposit.

The "day" feature represents the last contact day of the month. It is a numeric variable that can take values from 1 to 31. To determine the appropriate ranges of values, we need to consider common banking practices and customer behavior.

Typically, people receive their monthly salaries around the beginning of the month. They may be more likely to make financial decisions, such as subscribing to a term deposit, soon after receiving their salaries. Therefore, we can expect higher subscription rates at the beginning of the month.

Additionally, people may be more likely to make financial decisions towards the end of the month if they receive reminders or incentives from the bank. Therefore, we can also expect higher subscription rates towards the end of the month.

Based on this analysis, we can select typical day values for each target class:

For the "no" class (client does not subscribe to a term deposit):
- 2.0
- 12.0
- 16.0
- 20.0
- 24.0

For the "yes" class (client subscribes to a term deposit):
- 5.0
- 9.0
- 15.0
- 21.0
- 29.0

Now, let's create the dictionary with the requested format:

```json
{
	"no": [2.0, 12.0, 16.0, 20.0, 24.0],
	"yes": [5.0, 9.0, 15.0, 21.0, 29.0]
}
```