Based on prior knowledge, it is difficult to precisely predict how the type of job (feature: job) can directly influence the client's decision to subscribe to a term deposit (target: Does this client subscribe to a term deposit? Yes or no?). However, we can still analyze any possible relationship between the two.

To perform the analysis, we can look at the distribution of job types for clients who subscribed to a term deposit (target: yes) and those who did not (target: no). This will provide information on whether certain job types are more likely to lead to a subscription.

Here is the analysis of the relationship between the job feature and the target subscription status:

- For clients who did not subscribe (target: no):
  - Possible values of job: ['management', 'technician', 'entrepreneur', 'blue-collar', 'unknown', 'retired', 'admin.', 'services', 'self-employed', 'unemployed', 'housemaid', 'student'].
 
- For clients who subscribed (target: yes):
  - Possible values of job: ['management', 'technician', 'entrepreneur', 'blue-collar', 'unknown', 'retired', 'admin.', 'services', 'self-employed', 'unemployed', 'housemaid', 'student'].

Please find the dictionary with the relationship between the job feature and the target subscription status below:

```json
{
  "no": ["management", "technician", "entrepreneur", "blue-collar", "unknown", "retired", "admin.", "services", "self-employed", "unemployed", "housemaid", "student"],
  "yes": ["management", "technician", "entrepreneur", "blue-collar", "unknown", "retired", "admin.", "services", "self-employed", "unemployed", "housemaid", "student"]
}
```
Note that all possible job values are included for both target classes (yes and no) since it is difficult to determine which job types may have a significant impact on the subscription behavior.