Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

A series of interactive lab notebooks we prepared for the ACA course on "Internal Audit Knowledge Elements". The content of the series is based on Python, IPython Notebook, and PyTorch.

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on J…

DS Notebooks

Project for the Big Data Computing course at the University of "La Sapienza" in Master in Computer Science A.A. 2021/2022

IEEE Fraud Detection with XGBoost and CatBoost

This notebook tries to make fraud/not fraud predictions on a transactions dataset with highly imbalanced data.

Detecting fraudulent credit card transactions using machine learning.

IEEE-CIS Fraud Detection Kaggle Competition notebooks

🛡️ SecureCard-AI: A high-performance credit card fraud detection system implemented in a Jupyter Notebook, achieving 99.97% accuracy.

Fraud Detection in E-Commerce (SQL)

Fraud Detection using XGBoost on a 6M+ transaction dataset with 97% ROC-AUC. (Business insights also included.)

Repository to share the development of three machine learning tasks as part of the Cognorise Internship Data Science program

The notebook on the main topic of interpretable machine learning is a descriptive and instructive analysis of a car data set from a public source.

This is a notebook for fraud detection for a kaggle challenge.

This repository contains some of my machine learning notebooks I created on Kaggle

Survey notebook to compare traditional ML and deep learning techniques for fraud detection

ML portfolio project: fraud detection with reproducible notebooks, model card, datasheet, and tuned decision threshold.

ML-powered fraud detection for UPI transactions | 87% F1-Score | XGBoost + Flask | Real-time predictions | Interactive notebooks