Credit Card Fraud Detection Using Ensemble Machine Learning Techniques

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Credit Card Fraud Detection Using Ensemble Machine Learning Techniques

Original price was: ₹6,999.00.Current price is: ₹3,999.00. inc GSTs*

The massive volume of credit card transactions combined with the extreme scarcity of fraudulent samples makes traditional classification models prone to high false-positive rates. Systems must balance high sensitivity for fraud with low user friction through advanced ensemble learning and sampling techniques.

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This project employs ensemble methods, including XGBoost, LightGBM, and Isolation Forests, to detect fraudulent transactions in real-time. The system utilizes SMOTE (Synthetic Minority Over-sampling Technique) to handle the class imbalance inherent in financial datasets. It processes transactional features such as amount, location, and merchant category to identify deviations from a user’s normal spending profile. The outcome is a high-precision fraud scoring engine that helps financial institutions minimize losses while ensuring legitimate customers experience minimal transaction declines.

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Original price was: ₹6,999.00.Current price is: ₹3,999.00. inc GSTs*
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