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This project aims to identify fraudulent transactions using a real-world dataset containing credit card transaction data. Fraud detection is a highly imbalanced classification problem where fraudulent transactions are significantly rare compared to genuine ones.
AI-powered system to detect fraudulent transactions in e-commerce using machine learning. Includes data preprocessing, feature engineering, and classification models like Random Forest and XGBoost. Achieved high accuracy with interpretable results for real-time detection.
Deep learning project comparing CNN and MobileNetV2 for image classification on a small, imbalanced dataset. Includes preprocessing, augmentation, training, evaluation, and performance analysis using Python, TensorFlow, Keras, and Scikit-learn.