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Credit card fraud is a significant problem, with billions of dollars lost each year. Machine learning can be used to detect credit card fraud by identifying patterns that are indicative of fraudulent transactions. Credit card fraud refers to the physical loss of a credit card or the loss of sensitive credit card information.
🤖 The Fraud Credit Card Detection project aims to identify fraudulent transactions in credit card usage through the application of various machine learning methods, thereby improving the security of credit card operations and safeguarding users' financial assets.
In this project, we will analyse customer-level data which has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group. The dataset is taken from the Kaggle Website website and it has a total of 2,84,807 transactions, out of which 492 are fraudulent.
iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data
Implementation of an intelligence system to detect the fraud cases on the basis of classification.
Machine learning for credit card fraud detection (ML for CCFD) has become an active research field. This is illustrated by the remarkable amount of publications on the topic in the last decade.
This project aims to build a model to detect fraudulent credit card transactions in real-time. The dataset used in this project contains transactions made by credit cards in September 2023 by European cardholders.
A web app featuring five classification projects: Spam Mail Prediction, Titanic Survival Prediction, Wine Quality Prediction, Loan Status Prediction, and Credit Card Fraud Detection, all built with Streamlit.
🤖 The Fraud Credit Card Detection project aims to identify fraudulent transactions in credit card usage through the application of various machine learning methods, thereby improving the security of credit card operations and safeguarding users' financial assets.
This is a mini project on the credit card fraud detection in collaboration with Technocolabs.