A comprehensive .NET Core client library for Keycloak that provides seamless integration with Keycloak's authentication and authorization services. This library offers a robust implementation of Keycloak's REST API, including support for OpenID Connect, OAuth 2.0, and User-Managed Access (UMA 2.0).

💴 A machine learning project that detects fraudulent credit card transactions using classification algorithms. Includes data preprocessing, EDA, model training & evaluation with techniques like Random Forest, Logistic Regression, and SMOTE for class imbalance. Built for secure financial insights and real-world fraud detection use cases.

FraudDetectAI is an advanced credit card fraud detection system built with XGBoost and Hybrid SMOTE Sampling (Oversampling + Undersampling). This project tackles highly imbalanced datasets, ensuring strong fraud detection accuracy while minimizing overfitting risks.

Machine Learning pipeline for financial transaction fraud detection. Incorporates SMOTE, ensemble models, neural networks.

The Credit Card Fraud Detection project utilizes machine learning algorithms to identify and prevent fraudulent credit card transactions. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials