Skip to content
#

auprc

Here are 3 public repositories matching this topic...

Language: All
Filter by language

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

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.

  • Updated Nov 4, 2018
  • Jupyter Notebook

Improve this page

Add a description, image, and links to the auprc topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the auprc topic, visit your repo's landing page and select "manage topics."

Learn more