Based on prior knowledge, we can analyze the relationship between the credit_amount feature and the task of whether a person receives a credit or not. 

Potential Analysis:
1. Individuals with higher credit_amount may have a higher chance of receiving credit, as they may be deemed more creditworthy.
2. Individuals with lower credit_amount may have a lower chance of receiving credit, as they may be seen as less creditworthy.
3. There may be some overlap in the credit_amount values between the "yes" and "no" classes, as other factors can also play a role in determining creditworthiness.

Based on this analysis, we can create a dictionary that includes typical credit_amount values for each target class:

```json
{
	"yes": [5000.0, 10000.0, 20000.0, 30000.0, 40000.0],
	"no": [1000.0, 2000.0, 3000.0, 4000.0, 6000.0]
}
```

Please note that these credit_amount values are just examples and may vary depending on the specific dataset you are working with.