Based on prior knowledge, we can assume that the employment status of a person can have an impact on whether they receive credit or not. Let's analyze the relationship between the "employment" feature and the target variable.

To do this, we can examine the proportion of individuals in each employment category who receive credit. This will help us determine if there is any significant difference between the employment categories in terms of credit approval.

Here is the analysis:

- For the "yes" class (person receives credit):
  - Possible employment values: ['>=7', '1<=X<4', '4<=X<7']
- For the "no" class (person does not receive credit):
  - Possible employment values: ['unemployed', '<1']

Based on this analysis, we can create the following dictionary:

```json
{
	"yes": [">=7", "1<=X<4", "4<=X<7"],
	"no": ["unemployed", "<1"]
}
```

Please note that there are values in the "employment" feature that are not included in the dictionary. This is because they are difficult to predict or their relationship with the target variable is unclear.