Bank card fraud detection using machine learning. Web application using Streamlit framework
M5Stack Cardputer interface for the FraudTagger API
Ethereum fraud transaction detection using machine learning
Fraud Detection for e-commerce and Bank Transactions
To identify online payment fraud with machine learning, we need to train a machine learning model for classifying fraudulent and non-fraudulent payments. For this, we need a dataset containing information about online payment fraud, so that we can understand what type of transactions lead to fraud.
🛡️ Welcome to our Credit Card Fraud Detection project! 💳 Harnessing the formidable prowess machine learning, we're steadfast in our mission to fortify your financial stronghold against deceitful adversaries. Join our crusade for financial resilience,Ensuring every transaction is securely monitored! 🔐💯
This project demonstrates the use of a Self-Organizing Map (SOM) for fraud detection in a dataset. The dataset contains transaction records, and the goal is to identify potential fraudulent transactions using unsupervised learning techniques.
ENSAE-ENSAI Formation Continue (Cepe)/OpenClassrooms Data Analyst 2022-2023 - Projet 10
A credit card fraud detection system using Machine learning (Early project-2022)
Machine learning-based fraud detection system capable of identifying and preventing fraudulent transactions in real-time for Finex, a financial service provider based in Florida.
ML-based system to detect fraudulent credit card transactions with cost-benefit analysis.
Python note book for fraud detection and risk assessment in finance section
This is a fraud detection algorithm that is designed to detect fraudulent activity in online in-game markets using a combination of unsupervised learning and a rule based approach.
Machine learning projects focused on fraud detection.
End-to-end MLOps: SageMaker training, Model Registry, CI/CD, Guardrails, API Gateway.
Built a Fraud Detection System using ANN to identify fraudulent transactions with high accuracy. Optimized feature engineering and model performance, ensuring robust anomaly detection. Documented the process in Jupyter Notebooks for transparency and reproducibility.
Machine learning project for detecting fraudulent credit card transactions using imbalanced dataset handling and classification models.
Real-time fraud detection architecture powered by AWS Kinesis, KaggleHub, and SMOTE-augmented data — the foundation of DARIA, the Detection And Risk-Intelligence Agent.
Credit Score Classification: This repository features a machine learning project aimed at predicting credit scores based on financial data. Using advanced models like Random Forest and Gradient Boosting.
An XGBoost-based fraud detection modelto identify money laundering in mobile transactions using PaySim synthetic dataset.
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