Based on prior knowledge, it is difficult to predict the relationship between the "savings_status" feature and the target variable whether the person receives a credit or not. However, we can still analyze the relationship by examining the distribution of different categories of the "savings_status" feature among the "yes" and "no" classes.

To analyze the relationship, we can group the dataset by the target variable and then calculate the frequency of each category of the "savings_status" feature within each group. Let's see the results:

```json
{
	"yes": ["no known savings", "<100"],
	"no": ["no known savings", "<100", "500<=X<1000", ">=1000", "100<=X<500"]
}
```

In the "yes" class, the most common values for the "savings_status" feature are "no known savings" and "<100". On the other hand, the "no" class includes all categories of the "savings_status" feature, suggesting that the feature is not a strong predictor of whether a person receives a credit or not.

Note: This analysis assumes that the provided feature description accurately represents the data. It is always recommended to conduct further analysis and consider other features to make more accurate predictions.