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

This is a classification problem to detect or classify the fraud with label 0 or 1. Class with label 1 means fraud is detected otherwise 0. The biggest challenge is to handle the imbalanced data set.

Golang wrapper for Prompt API's BIN Checker API

NPM package for Prompt API's BIN Checker API

Ruby package for Prompt API's BIN Checker API

Technocolabs Machine Learning Developer Internship Project 2

Classifying fraudulent transactions using K-Means SMOTE and ANN

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

CreditCardFraudDetection

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

Credit-Card-Fraud-Detection-System - 6th Semester College Project

Use of different classification models to detect credit card frauds

This project aims at creating a classifier. It detects whether or not the card transaction is valid. Diverse machine learning algorithms are applied in this project to distinguish between a non-fraudulent and fraudulent transactions.

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.

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

This project uses logistic regression models to analyze credit risk. The recommended model, trained with resampled data, shows higher precision and recall scores for predicting high-risk loans. This model helps mitigate credit risk for lending companies.

This Python script uses machine learning models to detect fraudulent credit card transactions in a dataset. The dataset is loaded using the pandas library and preprocessed for machine learning by removing irrelevant features and rescaling the data.

Credit Card Fraud Detection is a crucial machine learning project with profound implications. It aims to safeguard financial transactions by identifying fraudulent activities. Leveraging advanced algorithms and historical transaction data, this project analyzes patterns and anomalies in credit card usage.