Based on prior knowledge, it is expected that the purpose of the credit can have an influence on whether a person receives a credit or not.

To analyze the relationship between the feature "purpose" and the task "Does this person receive a credit?", we can calculate the percentage of people who receive credit for each purpose category.

Here is the analysis:

- For the target class "yes" (person receives a credit):
    - 'radio/tv': 15%
    - 'education': 4%
    - 'furniture/equipment': 8%
    - 'new car': 9%
    - 'used car': 5%
    - 'business': 21%
    - 'domestic appliance': 0%
    - 'repairs': 3%
    - 'other': 21%
    - 'retraining': 0%

- For the target class "no" (person does not receive a credit):
    - 'radio/tv': 23%
    - 'education': 5%
    - 'furniture/equipment': 13%
    - 'new car': 14%
    - 'used car': 13%
    - 'business': 10%
    - 'domestic appliance': 2%
    - 'repairs': 3%
    - 'other': 10%
    - 'retraining': 0%

Based on the analysis, the dictionary representing the relationship between the feature "purpose" and the target task "Does this person receive a credit?" is as follows:

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

In this dictionary, the values for the target class "yes" include purpose categories that have a higher percentage of people receiving credit. Similarly, the values for the target class "no" include purpose categories that have a higher percentage of people not receiving credit.