Based on prior knowledge, we can analyze the relationship between the feature 'pdays' and the task of determining whether the client subscribes to a term deposit or not.

Analyzing the relationship between 'pdays' and the target variable, we can infer that if the 'pdays' value is high (indicating a larger number of days since the client was last contacted), it might be less likely for the client to subscribe to a term deposit. On the other hand, if the 'pdays' value is low (indicating a smaller number of days since the last contact), it might indicate a higher likelihood of subscribing to a term deposit.

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

```json
{
	"no": [999.0, 999.0, 999.0, 999.0, 999.0], 
	"yes": [1.0, 2.0, 3.0, 4.0, 5.0]
}
```

In this dictionary, we include 5 typical 'pdays' values for each target class. For the 'no' class, we use the value 999.0 to represent a high 'pdays' value, indicating that the client was not previously contacted or contacted a long time ago. For the 'yes' class, we include values 1.0, 2.0, 3.0, 4.0, and 5.0 to represent low 'pdays' values, indicating that the client was recently contacted.

Please note that this analysis is based on prior knowledge and assumptions. The actual relationship between 'pdays' and the target variable may vary depending on the dataset and specific circumstances.