In this challenge, we’ll take what we’ve learned in this chapter with a synthetic dataset and apply it to the case study data. We’ll see how an XGBoost model performs on a validation set and explain the model predictions using SHAP values. We have prepared the dataset for this activity by replacing the samples that had missing values for the PAY_1 feature, that we had previously ignored, while maintaining the same train/test split for the samples with no missing values. You can see how the data was prepared in the solution Notebook for this challenge in the next lesson.