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under-sampling

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Credit Fraud Detection of a highly imbalanced dataset of 280k transactions. Multiple ML algorithms(LogisticReg, ShallowNeuralNetwork, RandomForest, SVM, GradientBoosting) are compared for prediction purposes.

  • Updated Mar 27, 2024
  • Jupyter Notebook

Imbalanced data commonly exist in real world, especially in anomaly-detection tasks. Handling imbalanced data is important to the tasks, otherwise the predictions are biased towards the majority class. RandomUnderSampler, ClusterCentroids, CondensedNearestNeighbour, and etc. are useful undersampling tools to remove data for majority classes.

  • Updated Aug 27, 2023
  • Jupyter Notebook

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

  • Updated May 23, 2019
  • Jupyter Notebook

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