Based on prior knowledge, I can analyze the relationship between the "other_parties" feature and the target variable "Does this person receive a credit?". 

The "other_parties" feature represents the presence of other debtors or guarantors for the credit application, with three possible categories: "none", "guarantor", and "co applicant". 

To analyze the relationship, I will examine the distribution of the "other_parties" values for each target class.

Let's proceed with the analysis and generate the dictionary:

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

In this case, the "other_parties" feature seems to have a relationship with the target variable. 
- For individuals who receive credit ("yes" class), the presence of a "co applicant" seems to be a positive factor.
- On the other hand, for individuals who do not receive credit ("no" class), the absence of any "other_parties" ("none") or the presence of a "guarantor" seem to be more common.

Note that we do not include the value "co applicant" under the "no" class, as it is not specified whether having a "co applicant" would lead to denial of credit.