🛡️ Credit Card Fraud Detection using Machine Learning

This project detects fraudulent credit card transactions using machine learning techniques on a real, imbalanced dataset.

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🔍 Project Highlights

• Trained multiple models: Logistic Regression, Random Forest, XGBoost
• Balanced data using SMOTE to handle class imbalance
• Evaluated models using Precision, Recall, F1-Score, and ROC-AUC
• Visualized results with confusion matrix, ROC curve, and feature importance

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🧰 Technologies Used

• Python, Jupyter Notebook
• Pandas, NumPy, Matplotlib, Seaborn
• scikit-learn, imbalanced-learn, XGBoost

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📊 Sample Results

Metric	Score
Accuracy	99.72%
Precision	92.1%
Recall	88.3%
F1-Score	90.2%
ROC AUC	0.981