For a classification problem, when classes in the dependent variable are severely imbalanced (e.g. 90 yes, 10% no), training an efficient machine learning model becomes very difficult. However with SMOTE method, we can transform the data into a balaced form and train the model efficiently.
Here, we try to predict the credit card customers who are going to be default. The training dataset is imbalanced since a very small portion of the customers usually become default client. We will apply SMOTE to make the dataset balanced before training the model.