Based on the feature description and the task at hand, we are analyzing the relationship between the feature "other_parties" and the target variable which is whether the person receives a credit or not.

To do this analysis, we can start by examining the distribution of the different categories of "other_parties" for the two target classes:

- "none": This category implies that there are no other debtors or guarantors involved. It is likely that this category would be more common for individuals who do not receive credit.

- "guarantor": This category indicates the presence of a guarantor, who guarantees the repayment of the credit. It is possible that individuals with a guarantor have a higher likelihood of receiving credit.

- "co applicant": This category suggests the presence of a co-applicant for the credit. It is possible that having a co-applicant increases the chances of receiving credit.

Based on this analysis, we can create a dictionary with the possible values of "other_parties" for each target class:

```json
{
	"yes": ["guarantor", "co applicant"],
	"no": ["none"]
}
```

Note that we only included the values that are more likely to appear in each target class, and excluded the ones that are hard to predict or have insufficient information to make an informed decision.