Credit card fraud is a significant global issue, posing challenges for financial institutions due to the low incidence of fraud amid a high volume of legitimate transactions.

NPM package for Prompt API's BIN Checker API

Machine Learning for Credit Card Fraud Detection

Credit Card Fraud Detection using ML

the application of an intelligence system to classify and identify fraud situations.

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.

Data Science Internship at CodSoft

Use of different classification models to detect credit card frauds

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.

Titanic Survival Prediction, Movie Rating Prediction With Python, Iris Flower Classification, Sales Prediction Using Python, Credit Card Fraud Detection

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.

This repository contains all my Machine Learning projects.

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.

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

The objective of this project is to develop and utilize autoencoders for detecting anomalies in credit card transactions.

Using a self organizing map (SOM) to identify frauds in the credit card application process.

The aim of this project is to use the logistic regression mode as a binary classifier to analyse credit card risk. The recommended model helps to predict the high-risk cases. The accuracy, precision, and recall metrics are used to evaluate this model performance.

contains project related to python

CreditCardFraudDetection