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.

Videos

Each project comes with 2-5 hours of micro-videos explaining the solution.

Code & Dataset

Get access to 50+ solved projects with iPython notebooks and datasets.

Project Experience

Add project experience to your Linkedin/Github profiles.

What will you learn

Exploring the dataset

Perform EDA using Univariate, Bivariate and Multivariate analysis

Visualizing and understanding the feature plots and correlation plots

Create pairwise plots for each attribute

Create density plots for each attribute

Learn to handle imbalanced data using oversampling, undersampling and mixed sampling

Learn to remove redundant features

Rank features using LVQ model (Learning Vector Quantization)

Select features using RFE method (Recursive Feature Elimination)

Learn to preprocessing using LDA (Linear Discriminant Analysis)

Apply Linear Algorithms like Logistic Regression model

Apply Non Linear Algorithms like SVM (Support Vector Machine), KNN (K Nearest Neighbour) and Naive Bayes

Apply Non Linear Algorithms like CART (Classification and Regression Trees)

Apply Ensemble Algorithms like RandomForest, Bagging CART, Gradient Boosting model

Perform GLMNet Regression analysis

Apply Neural Network model

Compare results of different models

Select the best model

Visualize results using box and whisker plots

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

Similar Projects

Ecommerce product reviews - Pairwise ranking and sentiment analysis

This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

View Project Details