fraud-detection-model

🚨 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. 📊🔍

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• Jupyter Notebook

A universal oboi binary installer. Install oboi email fraud scoring into your command shell environment

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• Ruby

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.

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• Python

Graph neural network system that detects money laundering, fraud patterns, and security threats in blockchain transactions and smart contracts.

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• Python

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.

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• Jupyter Notebook

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

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• Jupyter Notebook