Imbalanced data techniques

In this project we are going to explore some methods to deal with imbalanced data by using the Bank Marketing Data Set. These techniques include the following data sampling methods:

1. Synthetic Minority Oversampling Technique (SMOTE)
2. Adaptive Synthetic (ADASYN)
3. Neighbourhood Cleaning Rule
4. One Sided Selection
5. SMOTEENN
6. Smote + Tomek

In addition to that, we will see how applying cost sensitive learning can affect the performance of the classifier. To read the full article with the description of the techniques applied, it can be found here.