💳 Credit Card Fraud Detection

Detect fraudulent credit card transactions using both supervised and unsupervised machine learning techniques.

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📁 Dataset

Source: Kaggle - Credit Card Fraud Detection

• Records: 284,807
• Frauds: 492 (0.172%)
• Features: 30 anonymized PCA features + Time + Amount
• Target Variable: Class (1 = Fraud, 0 = Not Fraud)

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🧰 Tools and Libraries

• Python
• NumPy, Pandas
• Matplotlib, Seaborn
• Scikit-learn
• imbalanced-learn (for supervised version)
• Joblib (for saving models)

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🧠 Approaches

✅ Supervised Learning

Detect fraud using labeled data. Algorithms used:

• Logistic Regression
• Decision Tree
• Random Forest
• XGBoost

✅ Unsupervised Learning

Detect fraud as anomalies without using labels in training. Algorithms used:

• Isolation Forest
• One-Class SVM
• Local Outlier Factor