Bank card fraud detection using machine learning. Web application using Streamlit framework

This project demonstrates the use of a Self-Organizing Map (SOM) for fraud detection in a dataset. The dataset contains transaction records, and the goal is to identify potential fraudulent transactions using unsupervised learning techniques.

This project focuses on detecting fraudulent credit card transactions using machine learning techniques. The goal is to predict whether a given transaction is legitimate or fraudulent based on various features of the transaction.

ENSAE-ENSAI Formation Continue (Cepe)/OpenClassrooms Data Analyst 2022-2023 - Projet 10

Detecting fraudulent credit card transactions using machine learning techniques, with a focus on handling imbalanced datasets.

AI-powered system to detect and flag suspicious financial transactions in real time using Python, Excel, and SQL Server. Includes automated form validation, unsupervised anomaly detection (Isolation Forest), and live dashboard integration.