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

Analysis:

1. It is expected that individuals with a lower number of existing credits at the bank are more likely to receive a credit. Hence, the feature values for the target class "yes" may fall in the lower range of the existing_credits values.

2. Conversely, individuals with a higher number of existing credits at the bank may be less likely to receive a credit. Therefore, the feature values for the target class "no" may fall in the higher range of the existing_credits values.

Based on this analysis, we can create a dictionary as follows:

```json
{
	"yes": [0.0, 0.5, 1.0, 1.5, 2.0],
	"no": [3.0, 3.5, 4.0, 4.5, 5.0]
}
```

In this example, I have included 5 typical existing_credits values (float values) for each target class. Please note that these values are just placeholders and need to be updated with actual values from the dataset for accurate analysis.