creditcardfrauddetection

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

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

Golang wrapper for Prompt API's BIN Checker API

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

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.

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.

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.

Ruby package for Prompt API's BIN Checker API

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.

Technocolabs Machine Learning Developer Internship Project 2

Reactive AI Agent for Fraud Detection

Machine Learning project for detecting fraudulent credit card transactions using Python, data preprocessing, and classification techniques.

CreditCardFraudDetection

Classifying fraudulent transactions using K-Means SMOTE and ANN

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.

NPM package for Prompt API's BIN Checker API

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