Based on the feature description, we can analyze the relationship between the "purpose" feature and the target variable "Does this person receive a credit?". 

To do this, we can examine the distribution of purpose values for each target class (yes and no). If there are distinct patterns or variations in the purpose values for each class, it indicates a potential relationship between the feature and the target.

Here's the analysis:

- For the target class "yes", the possible values of the "purpose" feature are:
	- radio/tv
	- education
	- furniture/equipment
	- new car
	- used car
	- business
	- domestic appliance
	- repairs
	- retraining

- For the target class "no", the possible values of the "purpose" feature are:
	- radio/tv
	- education
	- furniture/equipment
	- new car
	- used car
	- business
	- domestic appliance
	- repairs
	- other
	- retraining

Based on this analysis, we can generate the dictionary:

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

Please note that for the "no" class, there is an additional category "other" compared to the "yes" class. This indicates that there might be cases where the purpose value is "other" for the target class "no", but we cannot predict the exact purpose.