Credit Card Fraud Detection as a Classification Problem

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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Code & Dataset

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Project Experience

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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 ( 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 and

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Curriculum For This Mini Project

Loading the dataset
Understanding the Data
Exploratory Data Analysis (EDA)
Cross Validation
Business Aspect