![Capture d’écran 2022-12-06 à 12.29.55.png](jTGJQUZidnlq)

The prediction zone is built by the [interactive prediction scenario](scenario:PREDICT) whenever a user launches an analysis from the [Next Batch Prediction](article:8) tab.

 1. The [python recipe](recipe:compute_batch_and_sensor_for_prediction) prepares the data to match the user selection : it will get the last run batch from the selected equipment_id and append the selected batch parameters as new columns to match the expected input for the model.
 
 2. Then the row is [scored](recipe:score_batch_and_sensor_for_prediction) by the model that will output the failure risk prediction and [shapley values ](https://doc.dataiku.com/dss/latest/machine-learning/supervised/explanations.html#method-1-based-on-the-shapley-values).
 
 3. The[ group recipe](recipe:compute_sensor_data_for_interpretation) will filter the train data on the selected batch parameters and compute the average values for each sensor aggregation when it has led to a failure or a success. This information is used in the [Next Batch Prediction](article:8) tab to explain the prediction.