Bank card fraud detection using machine learning. Web application using Streamlit framework

Ethereum fraud transaction detection using machine learning

Fraud Detection for e-commerce and Bank Transactions

This project focuses on detecting fraudulent credit card transactions using machine learning techniques. The goal is to predict whether a given transaction is legitimate or fraudulent based on various features of the transaction.

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.

🛡️ 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! 🔐💯

An XGBoost-based fraud detection modelto identify money laundering in mobile transactions using PaySim synthetic dataset.

A machine learning project for detecting fraudulent transactions using Random Forest and XGBoost models, with data preprocessing and model evaluation.

Detecting fraudulent credit card transactions using machine learning techniques, with a focus on handling imbalanced datasets.

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.

A machine learning-based fraud detection system that preprocesses data, manages outliers, handles missing values, and mitigates multi-collinearity. It utilizes predictive modeling techniques (Logistic Regression, Random Forest, Gradient Boosting) and evaluates performance using precision, recall, F1-score, and ROC-AUC.

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.

Fraud detection in Insurance Companies

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.

An XGBoost-based fraud detection modelto identify money laundering in mobile transactions using PaySim synthetic dataset.