Chapter 12: Data Preparation for Fraud Analytics: Final Capstone: Credit Card Fraud

Published: 1 November 2023| Version 1 | DOI: 10.17632/zrxmphb3c9.1


In this project, we will analyze a credit card transaction dataset and apply machine learning techniques to detect fraudulent transactions. Credit card fraud is a pervasive issue that impacts both cardholders and financial institutions. Swift detection of fraudulent transactions can mitigate financial losses and bolster customer confidence. The dataset, named "creditcardfraud.csv", is an extensive collection of credit card transactions. It contains 5,050 transactions, of which 50 are fraudulent and 5,000 are legitimate. Each transaction is characterized by 30 features (V1–V28, Amount, and Class), where V1–V28 are anonymized features derived from a PCA transformation, 'Amount' represents the transaction amount, and 'Class' indicates if the transaction is fraudulent (1) or not (0).


Steps to reproduce

-Load and Analyze the Dataset: Import the credit card transaction dataset using Pandas and analyze its structure and distribution. -Python code: import pandas as pd df = pd.read_csv('creditcardfraud.csv') print( -Exploratory Data Analysis (EDA): Perform basic EDA to understand the data's structure and distribution. Calculate the ratio of fraudulent to non-fraudulent transactions to assess class imbalance. -Data Preprocessing: Apply preprocessing techniques, such as normalization and oversampling, to address class imbalance and prepare the data for machine learning. -Modeling: Split the dataset into training and testing sets and train a machine learning model, such as a Random Forest classifier, on the training set. -Model Evaluation: Evaluate the model's performance on the testing set using metrics like precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Identify the most important features contributing to the model's performance.


Machine Learning, Sampling, Credit Card Fraud