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
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.
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.
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.
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.
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.
In this Upgrad/IIIT-B Capstone project, we navigated the complex landscape of credit card fraud, employing advanced machine learning techniques to bolster banks against financial losses. With a focus on precision, we predicted fraudulent credit card transactions by analyzing customer-level data from Worldline and the Machine Learning Group.
NPM package for Prompt API's BIN Checker API
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.
Machine Learning for Credit Card Fraud Detection
Credit Card Fraud Detection using ML
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