Implementation of C4.5 + Binarization (OVO / OVA) with/without SMOTE preprocessing. This way, multi-class imbalanced problems can be addressed
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Updated
May 2, 2017 - Java
Implementation of C4.5 + Binarization (OVO / OVA) with/without SMOTE preprocessing. This way, multi-class imbalanced problems can be addressed
2017 repository
We use the widely available Kiva Dataset to predict if a Kiva Loan posting will get funded or not
Quick notebook to refer to different ways to handle imbalanced datasets.
Basic POC to showcase how to sample data for an imbalanced ad-clicks predictive model.
Predicting credit card defaults (Classification)
Predicting vessel transshipment (discharge) amounts using XGBoost regression trees on a very small dataset.
Implementation of semi and self supervised learning on Imbalanced Dataset
This python code is an individual work and also my thesis work which contains a loss function with penalty to solve the problem of imbalanced classes and also the simple ANN to detect if a transaction is fraudulent or not in the case of credit card fraud. So with this algorithm, we can easily detect fraudulent transaction with a good precision .
LightGBM no-show predictor + AWS deployment (EC2 + Elastic Beanstalk)
Classification on an imbalanced dataset, evaluating several model-resampling method combinations with hyperparameter tuning.
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