Based on prior knowledge, I can analyze the relationship between the feature "month" and the target variable "Does this client subscribe to a term deposit?"

To analyze this relationship, we need to consider the distribution of the "month" feature in both the "yes" and "no" classes. By examining the data, we can identify the possible values of the "month" feature for each target class.

Here is the analysis and the corresponding dictionary:

```json
{
	"no": ["may", "jun", "jul", "aug", "oct", "nov"],  
	"yes": ["mar", "apr", "sep", "oct", "nov", "dec"]
}
```

Explanation:
- For the "no" class, the possible values of the "month" feature are: "may", "jun", "jul", "aug", "oct", and "nov".
- For the "yes" class, the possible values of the "month" feature are: "mar", "apr", "sep", "oct", "nov", and "dec".

Note: It is not necessary to include all months in the analysis if they have a very low frequency and are hard to predict. In this case, it seems that "jan" and "feb" are likely to have low frequencies for both classes, so they are not included in the dictionary.