Project 4: Credit Card Fraud Detection

Detect fraudulent credit card transactions.

Starter code: Run starter.py after placing creditcard.csv in data/.

Difficulty

Intermediate

Time Estimate

4-5 days

Skills You'll Practice

• Anomaly Detection
• Imbalanced Data Handling
• Classification
• Feature Engineering
• Precision/Recall Tradeoffs

Learning Objectives

By completing this project, you will learn to:

• Handle highly imbalanced data
• Apply anomaly detection techniques
• Balance precision and recall
• Use cost-sensitive learning
• Engineer features for fraud detection

Dataset

Credit Card Fraud Detection

• Kaggle Credit Card Fraud
• Highly imbalanced (0.17% fraud)
• Anonymized features (PCA transformed)
• Time and amount features

Project Steps

Step 1: Load and Explore Data

• Load dataset
• Analyze class distribution (highly imbalanced!)
• Check for missing values
• Explore feature distributions
• Analyze fraud patterns

Step 2: Handle Imbalance

• Prefer class weights before aggressive resampling
• If you use SMOTE (or similar), apply it only inside training folds (never on the full dataset before splitting)
• Undersampling and different sampling strategies as ablations

Step 3: Feature Engineering

• Create time-based features
• Transform amount feature
• Create interaction features
• Feature scaling

Step 4: Anomaly Detection

• Isolation Forest
• Local Outlier Factor (LOF)
• One-Class SVM
• Compare with classification

Step 5: Model Training

• Train multiple models:
  • Logistic Regression
  • Random Forest
  • XGBoost
  • Isolation Forest
• Use cross-validation

Step 6: Model Evaluation

• Optimize a cost-sensitive threshold (false alarms cost ops time; misses cost fraud loss)
• Report precision, recall, PR-AUC (not accuracy)
• ROC-AUC as a secondary view
• Precision-Recall curve
• Cost-benefit analysis

Step 7: Threshold Tuning

• Adjust classification threshold
• Balance precision and recall
• Calculate cost of errors
• Choose optimal threshold

Expected Deliverables

1. Jupyter Notebook with complete analysis
2. Model with high recall for fraud
3. Evaluation Report with metrics and tradeoffs
4. Cost Analysis of different thresholds

Evaluation Metrics

• Recall: catch actual fraud (misses are expensive)
• Precision: bound alert load / false alarms (also expensive in ops)
• PR-AUC / F1: summarize the trade-off; pick a threshold from cost, not from a slogan
• ROC-AUC: secondary overall ranking metric
• Cost: explicit $ impact of FP vs FN if you can estimate it

Key Challenges

• Extreme class imbalance (0.17% fraud)
• Need high recall (catch fraud)
• Need reasonable precision (avoid false alarms)
• Cost-sensitive problem

Tips

• Focus on recall first (catch fraud)
• Use anomaly detection techniques
• Try ensemble methods
• Carefully tune threshold
• Calculate cost of false negatives vs false positives
• Use stratified sampling for evaluation

Resources

• Kaggle Fraud Detection
• Imbalanced Learn
• Anomaly Detection Guide

Extensions

• Real-time fraud detection system
• Explainable AI for fraud cases
• Cost-benefit analysis dashboard
• Deploy as API service

Next Steps

After completing this project:

• Try other fraud datasets
• Experiment with deep learning
• Move to Project 5: Customer Segmentation