Fraud detection using Deep Neural Networks to predict fraudulent transactions in financial data. 🚨🤖 Complete process from EDA and data preprocessing to model training and evaluation. 📊🔍

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 aims to develop a machine learning model for detecting fraudulent credit card transactions. By leveraging various data analysis techniques and machine learning algorithms, we can effectively identify and classify transactions as legitimate or fraudulent.