🚨 Fraud Detection with Deep Neural Networks (PoC) 🤖 A hands-on personal project to predict fraudulent financial transactions using deep learning. Covers the full pipeline: from exploratory data analysis (EDA) and preprocessing to model training and evaluation. An experimental approach to tackling real-world financial fraud. 📊🔍

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.

Basic machine learning pipeline models designed to classify bank transactions as "IsFraud" or "NotFraud" while minimizing false positives.

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.