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sharmaroshan / Fraud-Detection-in-Online-Transactions

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Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting

finance machine-learning query deep-learning data-visualization data-analytics classification data-analysis sampling confusion-matrix large-dataset classification-report anamoly-detection auprc under-sampling over-sampling
  • Updated May 23, 2019
  • Jupyter Notebook

north0n-FI / Predicting-Fraud-in-Financial-Payment-Services

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Trying to recogize and predict fraud in financial transactions is a good example of binary classification analysis. A transaction either is fraudulent, or it is genuine. What makes fraud detection especially challenging is the is the highly imbalanced distribution between positive (genuine) and negative (fraud) classes.

xgboost binary-classification fraud-detection skewed-data auprc
  • Updated Nov 4, 2018
  • Jupyter Notebook

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