There are 1 repository under borderline-smote topic.
Detecting Abnormal Markets - Early Warning Systems
Develop a model to predict which retail customers will respond to a marketing campaign. Logistic Regression shows the best performance.
Used CDC dataset for heart attack detection in patients. Balanced the dataset using SMOTE and Borderline SMOTE and used feature selection and machine learning to create different models and compared them based on metrics such as F-1 score, ROC AUC, MCC, and accuracy.
Leveraging and comparing various ML techniques to forecast credit card defaults [Imbalanced data]
demonstrate different models such as Variational Autoencoders and GANs in a variety of datasets, including tabular, text and image data, including the generation of synthetic data for comparison of their effectiveness in all models for each kind of dataset