```json
{
	"no": [0.0, 1.0, 2.0, 3.0, 4.0],
	"yes": [0.0, 1.0, 2.0, 3.0, 4.0]
}
```

Based on the given feature "previous" which represents the number of contacts performed before this campaign and for this client, we can analyze its relationship with the target variable "Does this client subscribe to a term deposit?". 

Analyzing the data, we find that for both target classes "no" and "yes", there are typical values of previous contacts that occur. Hence, in the dictionary provided above, 5 typical values of "previous" for each target class have been included.

For the target class "no", the typical values of previous contacts are [0.0, 1.0, 2.0, 3.0, 4.0]. This suggests that clients who did not subscribe to a term deposit in the previous campaign tend to have a range of previous contacts from 0 to 4 before this campaign.

Similarly, for the target class "yes", the typical values of previous contacts are [0.0, 1.0, 2.0, 3.0, 4.0]. This indicates that clients who did subscribe to a term deposit in the previous campaign also tend to have a range of previous contacts from 0 to 4 before this campaign.

However, since the range and values of "previous" are not explicitly provided, this analysis is based on prior knowledge and the assumption that a range from 0 to 4 may represent typical values for this feature. The actual range and values might vary depending on the dataset and domain knowledge.