Implementation of an intelligence system to detect the fraud cases on the basis of classification.

This repo contains 4 different projects. Built various machine learning models for Kaggle competitions. Also carried out Exploratory Data Analysis, Data Cleaning, Data Visualization, Data Munging, Feature Selection etc

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An attempt to detect fraud in online transaction in deep neural network using pytorch

Credit Card Fraud Detection using machine learning

Credit Card Fraud detection System Using Artificial Intelligence and Machine learning.

Credit Card Fraud Prediction in ASP.NET Core using ML.NET

Although digital transactions in India registered a 51% growth in 2018-19, their safety remains a concern. Fraudulent activities have increased severalfold, with around 52,304 cases of credit/debit card fraud reported in FY'19 alone. Due to this steep increase in banking frauds, it is the need of the hour to detect these fraudulent transactions in time in order to help consumers as well as banks, who are losing their credit worth each day. Machine learning can play a vital role in detecting fraudulent transactions. Imagine you get a call from your bank, and the customer care executive informs you that your card is about to expire in a week. Immediately, you check your card details and realise that it will expire in the next 8 days. Now, in order to renew your membership, the executive asks you to verify a few details such as your credit card number, the expiry date and the CVV number. Will you share these details with the executive? In such situations, you need to be careful because the details that you might share with them could grant them unhindered access to your credit card account.The aim of this project is to predict fraudulent credit card transactions using machine learning models. The data set that you will be working on during this project was obtained from Kaggle. It contains thousands of individual transactions that took place over a course of two days and their respective labels.

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Developed an advanced GCN-based Credit Card Fraud Detection system using cutting-edge deep learning techniques. Achieved high accuracy in real-time detection and prevention of fraudulent transactions, ensuring robust security and risk mitigation in the finance industry.

The task is to classify credit card transactions as fraudulent or legitimate.

IS525E Data Science for Business — Credit Card Fraud Detection using ML (Finance domain)

A machine learning project to detect fraudulent credit card transactions using real-world data.

Creditcard validator by Using Luhn's algorithm to verify the validity of credit card numbers. It also fetches credit card number data from datasets for testing and analysis.

Golang wrapper for Prompt API's BIN Checker API

This repository contains an implementation of credit card fault detection using Luhn's algorithm. Luhn's algorithm is a checksum formula used to validate credit card numbers, as well as other identification numbers. The algorithm is based on performing a set of arithmetic operations on the digits of a given number, resulting in a checksum value.

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This project is focused on building a Credit Card Fraud Detection System that utilizes machine learning techniques to detect fraudulent transactions. The project will incorporate MLOps practices to ensure the entire lifecycle of the machine learning model is automated and scalable.

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Tutorial for credit card fraud detection

Repository for completing the Kaggle Credit Card Fraud Detection competition

Kaggle Competiton: Using Anomaly detection technique on a highly imbalanced high dimensional data to detect fraud. Used variety of technique including SMOTE to balance the data set and other machine learning technique to build the model.

This repository demonstrates how to build a robust fraud detection system that combines supervised learning techniques with anomaly detection models. It provides end-to-end implementation, from data preprocessing and model training to deploying a real-time fraud detection API using FastAPI.

Detect credit card fraud with machine learning

Credit Card Fraud Detection

El proyecto trata sobre el desarrollo de un modelo de predicción de fraude para una institución financiera utilizando datos históricos de tarjetas de crédito.

functions to preprocess data, train and test various classifiers on the CreditCard Fraud Dataset (available in Kaggle) and also apply clustering algorithms