Based on prior knowledge and without access to the data, it is difficult to determine the exact ranges of credit_amount that correspond to the target class. However, I can provide a general analysis of the relationship between credit_amount and the task of determining whether a person receives credit or not.

Analyzing the relationship between credit_amount and the target variable (receiving credit), we can make some assumptions:

1. Higher credit_amount may indicate a higher likelihood of receiving credit.
2. Lower credit_amount may indicate a lower likelihood of receiving credit.

With these assumptions in mind, I will generate a dictionary with example credit_amount values for each target class, 'yes' and 'no'.

```json
{
	"yes": [1000.0, 2000.0, 3000.0, 4000.0, 5000.0],
	"no": [500.0, 1000.0, 1500.0, 2000.0, 2500.0]
}
```

Please note that these are just example values and the actual credit_amount ranges for each target class should be determined using the actual data.