This repository contains a Fraud Detection project that utilizes Deep Learning and Self-Organizing Maps (SOM) Neural Networks to identify fraudulent activities in financial transactions. The project focuses on data preprocessing, visualization, and model training using advanced machine learning techniques.
Deep Learning Approach: Implements SOM, an unsupervised neural network for anomaly detection.
Data Processing: Uses Pandas and NumPy for handling transaction data.
Visualization: Leverages Matplotlib, Seaborn, and Plotly for data insights.
Feature Scaling & Clustering: Prepares data for effective fraud detection.