Recall and Precision at K Examples

Create a usage example on how these ranking metrics can be used:

```+------------------------------------------------------------+-------------------------------------------------------------------------------+
| Python API | Description |
+============================================================+===============================================================================+
| `metriks.recall_at_k(y_true, y_prob, k)` | Calculates recall at k for binary classification ranking problems. |
+------------------------------------------------------------+-------------------------------------------------------------------------------+
| `metriks.precision_at_k(y_true, y_prob, k)` | Calculates precision at k for binary classification ranking problems. |
+------------------------------------------------------------+-------------------------------------------------------------------------------+
```

1. Identify a dataset that can be used to train a ranking model
2. Train a ranking model with the data
3. Use the given metrics above and show results and demonstrate how these metrics can be used

Start by reviewing the Python API entries for metriks.recall_at_k(y_true, y_prob, k) and metriks.precision_at_k(y_true, y_prob, k), then identify a suitable ranking dataset and model. Done means a usage example documents the dataset, training process, metric calls, and resulting values.

Written by the indexing model from the issue text.