Credit Card Fraud Detection
Detect fraudulent credit card transactions using machine learning with high precision and recall.
Duration
1 weeks
Team
2 members
Accuracy
96.78%
Technologies Used
Project Overview
A comprehensive ML project to detect fraudulent transactions using real-world credit card data. It tackles class imbalance using SMOTE, reduces dimensionality with PCA, and leverages powerful classifiers like XGBoost and Random Forest to ensure accurate and fast detection.
Methodology
Challenges
Highly imbalanced dataset (fraud vs non-fraud)
Selecting features that enhance fraud prediction
Maintaining high accuracy without overfitting
Solutions
Applied SMOTE to balance the class distribution
Used PCA to retain 95% variance while reducing feature space
Evaluated models with multiple metrics for robustness
Results & Impact
Achieved 96.78% accuracy with XGBoost
F1 Score of 0.78, Recall of 0.80 on test data
Models exported for future deployment
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