RCSMOTE: Range-Controlled Synthetic Minority Over-sampling Technique for handling the class imbalance problem
Detecting credit card fraud using a neural network model on highly imbalanced classes.
This project employs advanced SMOTE variants to effectively work on imbalanced classification challenges in machine learning datasets.
My take on "Do not overfit! II" competition on Kaggle which challenges participants to avoid overfitting.
Image classification pipeline relying on Deep Learning models dealing with training over multiple datasets with unbalanced labels and classes. In addition, also classification using histogram data was done for better generalization over different datasets.
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