For this lab, we will build a model on customer churn binary classification problem. You will be using files_for_lab/Customer-Churn.csv file.

1. Apply SMOTE for upsampling the data

   • Use logistic regression to fit the model and compute the accuracy of the model.
   • Use decision tree classifier to fit the model and compute the accuracy of the model.
   • Compare the accuracies of the two models.

2. Apply TomekLinks for downsampling

   • It is important to remember that it does not make the two classes equal but only removes the points from the majority class that are close to other points in minority class.
   • Use logistic regression to fit the model and compute the accuracy of the model.
   • Use decision tree classifier to fit the model and compute the accuracy of the model.
   • Compare the accuracies of the two models.
   • You can also apply this algorithm one more time and check the how the imbalance in the two classes changed from the last time.