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.
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
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 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.
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 repository presents a credit card fraud detection system utilizing a Logistic Regression model trained on a dataset of 284,807 transactions with significant class imbalance. After employing under-sampling for balance, the model achieves a test accuracy of around 93.40%, showcasing the effectiveness of ML in identifying fraudulent transactions.
contains project related to python
Machine Learning for Credit Card Fraud Detection
Credit Card Fraud Detection using ML
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