When dealing with imbalanced dataset, the notebook offers the generation of synthetic data by upsampling the minority class using SMOTE. Model training is performed with several classifiers and model interpretation is explored using Gini feature importance, 8 permutation feature importance, and SHapley Additive exPlanations (SHAP) analysis, which assesses the relative importance of features on the predictions.