2017 repository
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Updated
Nov 14, 2017 - Jupyter Notebook
2017 repository
Implementation of semi and self supervised learning on Imbalanced Dataset
LightGBM no-show predictor + AWS deployment (EC2 + Elastic Beanstalk)
Quick notebook to refer to different ways to handle imbalanced datasets.
We use the widely available Kiva Dataset to predict if a Kiva Loan posting will get funded or not
Predicting vessel transshipment (discharge) amounts using XGBoost regression trees on a very small 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 .
Basic POC to showcase how to sample data for an imbalanced ad-clicks predictive model.
Classification on an imbalanced dataset, evaluating several model-resampling method combinations with hyperparameter tuning.
Implementation of C4.5 + Binarization (OVO / OVA) with/without SMOTE preprocessing. This way, multi-class imbalanced problems can be addressed
Predicting credit card defaults (Classification)
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