No description available

No description available

🔍 Online Payments Fraud Detection using Machine Learning An end-to-end ML project that detects fraudulent transactions using real-world payment data. Features a high-performance XGBoost classifier, and a Flask-powered web app for real-time fraud prediction. Fully modular, scalable, and built for production-ready deployment.

Online payment fraud detection ML Project

No description available

No description available

No description available

No description available

Built an ML pipeline to detect fraudulent transactions using a PaySim-like dataset. Performed EDA, feature engineering, and imbalance handling (SMOTE) to improve model performance. Trained and evaluated models (Random Forest, XGBoost) — achieved high F1-score on fraud detection.

The Project implements ML algorithms for Online Payment Fraud detection

No description available

This repository contains Online Fraud Payment Detection project using Python, ML and exploratory data analysis.

"Online Payment Fraud Detection using ML. This project trains a Decision Tree model on transaction data to classify payments as Fraud or Not Fraud. It uses features like transaction type, amount, and account balances. Includes preprocessing, model training, and real-time prediction support."

Online-Payment-Fraud-Detection-ML is a machine learning project focused on detecting fraudulent online transactions. The model analyzes transaction patterns to distinguish between legitimate and fraudulent activities, aiming to enhance payment security and reduce financial risks.

With the rise of digital transactions, online payment fraud has become a significant concern for financial institutions and businesses. This project aims to develop a fraud detection system using Machine Learning (ML) in Python to identify fraudulent transactions and enhance security.

Online Payments Fraud Detection using Machine Learning is a project that detects fraudulent transactions by analyzing user behavior and transaction patterns. It uses ML algorithms to identify anomalies in real time, helping reduce financial losses and improve digital payment security.

The Credit Card Fraud Detection project is an AI/ML-based solution designed to identify fraudulent transactions in real time. With the rapid growth of online payments, detecting fraud has become critical for financial security. The project is Overall, the project demonstrates the power of machine learning in cybersecurity and financial safety.

AI/ML & DL-based fraud detection projects analyzing credit card and online payment datasets. They perform EDA, preprocessing, handle class imbalance, and train models like Random Forest, XGBoost, and neural networks to detect fraudulent transactions, evaluating with precision, recall, F1, and ROC-AUC.