Based on the feature "purpose" and the task "Does this person receive a credit?", we can analyze the relationship between the feature and the target by examining the different purposes listed and whether or not they are likely to result in a credit being issued.

After conducting a thorough analysis, we can create a dictionary that categorizes the purposes into two lists: one for when the target class is "yes" (indicating that the person receives a credit) and one for when the target class is "no" (indicating that the person does not receive a credit).

Here is the dictionary:

```json
{
	"yes": ["radio/tv", "education", "furniture/equipment", "new car", "used car", "business", "domestic appliance", "repairs"],
	"no": ["other", "retraining"]
}
```

Explanation:
- The "yes" list includes all the purposes that are more likely to result in a person receiving a credit. These purposes may involve purchases or investments that typically require financing.
- The "no" list includes purposes that are less likely to result in a person receiving a credit. These purposes may be more difficult to predict or may not align with traditional credit requirements.

Note:
- The values in the purpose lists are presented as strings in lists.
- For the purpose values that are hard to predict or not mentioned, we exclude them from the dictionary. However, we ensure that each target class has a non-empty list of purposes.