Data Preparation for Fraud Analytics: Lab Projects

Abdelrahim Al Aqqad ()

Chapter Chapter 10 in Fraud Analytics in Action, 2026, pp 233-237 from Springer

Abstract: Abstract This chapter bridges theoretical foundations and practical application through two structured laboratory projects in data preparation and fraud analytics. The first project centers on the IBM HR Analytics Employee Attrition and Performance dataset, guiding learners through end-to-end data wrangling and exploratory data analysis using Python’s Pandas library. Key tasks include handling missing values, applying one-hot encoding to categorical variables, scaling numerical features, and visualizing variable relationships through correlation matrices and heatmaps. The second project presents a capstone exercise in credit card fraud detection, using a dataset of 5,050 transactions—50 fraudulent and 5,000 legitimate—each described by 30 features. Learners apply machine learning techniques including Random Forest and logistic regression classifiers, evaluate model performance using precision, recall, and AUC-ROC metrics, and address class imbalance through oversampling. Post-lab reflections highlight the trade-offs between sensitivity and specificity in fraud detection models and propose enhancements including cost-sensitive learning, deep learning exploration, and domain expert feedback loops. By the chapter's end, learners have developed tangible, transferable skills in real-world data preparation and fraud analytics.

Date: 2026
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DOI: 10.1007/978-3-032-16023-2_10

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