Fraud Detection Case Study: How ML Finds Suspicious Transactions: Data Scientist Guide (2026) | Edugators

Fraud Detection Case Study: How ML Finds Suspicious Transactions in Data Scientist

Topic 3 of 5

Fraud Detection (Case Study)

Fraud is tricky because fraud cases are usually very few compared to normal cases (imbalanced data).

How I Handle It

Features: transaction frequency, amount spikes, location mismatch
Models: logistic regression, tree-based models
Metrics: precision/recall (accuracy is misleading)

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Recommendation System Case Study: Netflix/Amazon Style (Real World) Customer Churn Prediction Case Study: Retaining Users with Data

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