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

What will you get

Access to recording of the complete credit card fraud detection project

Access to all material related to the data science project like - credit card fraud detection project documentation, credit card transaction dataset, solution files etc.

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