Based on my prior knowledge, the relationship between the feature 'credit_amount' and the task of whether a person receives a credit can be analyzed as follows:

1. Individuals with lower credit amounts may have a higher chance of being approved for a credit.
2. It is possible that individuals with very high credit amounts are more likely to be approved for a credit.
3. There may be a range of credit amounts where the likelihood of receiving a credit is higher.

Based on these assumptions, I will create a dictionary with typical credit_amount values for each target class:

```json
{
	"yes": [1000.0, 1500.0, 2000.0, 2500.0, 3000.0],
	"no": [500.0, 750.0, 1250.0, 1750.0, 2250.0]
}
```

Please note that these values are only hypothetical and should be used as examples. The actual credit_amount values for each target class may vary depending on the specific dataset and analysis.