Based on the given feature description and task, the personal_status feature appears to be relevant for predicting whether a person receives credit or not. To analyze the relationship between personal_status and the target variable, we will examine the possible values of personal_status for each target class (yes and no).

Based on the provided categories for personal_status, we can infer the following:

- For the target class "yes" (person receives credit), the possible values of personal_status are: 'male single' and 'female div/dep/mar'.

- For the target class "no" (person does not receive credit), the possible values of personal_status are: 'male div/sep' and 'male mar/wid'.

Based on this analysis, we can create the following dictionary:

```json
{
	"yes": ["male single", "female div/dep/mar"],
	"no": ["male div/sep", "male mar/wid"]
}
```

Note that we do not include the "hard to predict" categories in the dictionary, as instructed.