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Credit Card Fraud Detection as a Classification Problem

In this project, we will predict the credit card fraud in the transactional dataset using some of the predictive model.
4.74.7

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What will you learn

  • Handle imbalance data
  • Creation classifier
  • Compare accuracy
  • Use deep learning to classify
  • Implementation using either R or Python

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • R 3.3.1 or latest R-Studio
  • Python 2.7

Project Description

The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset present transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

The dataset has been collected and analyzed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML

Instructors

 
Pradeepta