Based on my prior knowledge, I will analyze the relationship between the feature "job" and the task of whether a client subscribes to a term deposit.

To analyze the relationship, we can calculate the percentage of clients who subscribe to a term deposit (target variable "yes") for each category of the job feature.

Here is the analysis of the relationship between the "job" feature and the target variable:

- For job = 'management', the percentage of clients who subscribe to a term deposit is high.
- For job = 'technician', the percentage of clients who subscribe to a term deposit is average.
- For job = 'entrepreneur', the percentage of clients who subscribe to a term deposit is average.
- For job = 'blue-collar', the percentage of clients who subscribe to a term deposit is low.
- For job = 'unknown', the percentage of clients who subscribe to a term deposit is low.
- For job = 'retired', the percentage of clients who subscribe to a term deposit is high.
- For job = 'admin.', the percentage of clients who subscribe to a term deposit is average.
- For job = 'services', the percentage of clients who subscribe to a term deposit is average.
- For job = 'self-employed', the percentage of clients who subscribe to a term deposit is average.
- For job = 'unemployed', the percentage of clients who subscribe to a term deposit is low.
- For job = 'housemaid', the percentage of clients who subscribe to a term deposit is low.
- For job = 'student', the percentage of clients who subscribe to a term deposit is high.

Based on this analysis, the dictionary with the relationship between the "job" feature and the target variable is as follows:

```json
{
	"no": ["blue-collar", "unknown", "unemployed", "housemaid"],
	"yes": ["management", "retired", "student"]
}
```