Avishek2020/Handle-imbalanced-data

Get familiar with various techniques to handle the imbalanced class.

Handle-imbalanced-data

Get familiar with various techniques to handle the imbalanced class.

We have implemented the following imbalance data handeling techniques:

1. Random Under-Sampling
2. Random Over-Sampling
3. Random under-sampling with imblearn
4. Random over-sampling with imblearn
5. Under-sampling: Tomek links
6. Synthetic Minority Oversampling Technique (SMOTE)
7. NearMiss
8. Change the performance metric
9. Penalize Algorithms (Cost-Sensitive Training)
10. Change the algorithm