Graph-based analytics and HGT models to identify suspicious transaction networks (fraufulent transaction in fintech and digital wallets)
A real-time fraud detection dashboard built with Next.js, TypeScript, and Tailwind CSS, providing instant ML-powered analysis of credit card transactions with a sleek, responsive UI.
Machine Learning based Fraudulent Transaction Detection
Fraud Detection in Mobile Money Transactions using Machine Learning . A binary classification project comparing six models (Logistic Regression, Naive Bayes, Decision Tree, Random Forest, KNN, SVM) on the PaySim dataset. Includes data preprocessing, class balancing, feature importance analysis, and model evaluation (accuracy, precision, recall, F1-
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