Credit Card Fraud Detection as a Classification Problem

In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.
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What will you learn

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

Project Description

The Credit Card Fraud detection 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

Curriculum For This Mini Project

 
  Loading the dataset
03m
  Understanding the Data
03m
  Intro-to-eda
01m
  Exploratory Data Analysis (EDA)
11m
  PCA
03m
  Train-test
05m
  Cross Validation
03m
  LDA
06m
  Logistic_reg
03m
  SVM
03m
  Business Aspect
02m