Based on the feature description, the "pdays" feature represents the number of days that have passed since the client was last contacted in a previous campaign. To analyze its relationship with the target variable, "Does this client subscribe to a term deposit? Yes or no?", we can consider the following:

1. Clients who have never been contacted before (i.e., the previous campaign pdays value is 999) might have a lower chance of subscribing to a term deposit compared to clients who have been recently contacted.

2. Clients who have been contacted more recently (i.e., lower pdays value) might have a higher chance of subscribing to a term deposit compared to clients who have been contacted a long time ago.

3. If the pdays value is 0, it indicates that the client was contacted on the same day as the current campaign. This might suggest an increased likelihood of subscribing to a term deposit compared to clients who were contacted in previous campaigns.

Based on these assumptions, we can create a dictionary with the typical pdays values for each target class ('no' and 'yes'):

```json
{
    "no": [999.0, 999.0, 999.0, 999.0, 999.0],
    "yes": [0.0, 0.0, 0.0, 0.0, 1.0]
}
```

In this dictionary, we have selected 999 as the typical pdays value for the 'no' class, which represents clients who have never been contacted before. For the 'yes' class, we have selected 0 as a typical value, indicating that the client was contacted on the same day as the current campaign, and 1 to represent recently contacted clients.

Please note that these values are based on assumptions and prior knowledge, and further data analysis or modeling may be required for a more accurate understanding of the relationship between the pdays feature and the target variable.