A data science project to predict whether a transaction is a fraud or not.

💳 Creates a new gym environment for credit-card anomaly detection using Deep Q-Networks (DQN) and leverages Open AI's Gym toolkit to allocate appropriate awards to the RL agent.

System to tell apart the transaction was from the real user who owns the credit card or the transaction was from the stolen credit card.

Unified fraud detection and churn prediction platform with advanced ML pipelines, feature engineering, behavioral analytics, and production-ready inference.

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

💳 Creates a new gym environment for credit-card anomaly detection using Deep Q-Networks (DQN) and leverages Open AI's Gym toolkit to allocate appropriate awards to the RL agent.

Quantum Graph Neural Network Fraud Detection Project for Equal1

This is my final project for my internship at EISystems Technologies. I have used two ML algorithms and tried my hands-on. Also, the final report is included.

Building an online payment fraud detection system using machine learning algorithms. It utilizes three primary classification algorithms - Logistic Regression, Decision Tree, and Random Forest - to analyze and classify transactions as either legitimate or fraudulent.

A machine learning-based UPI fraud detection system that analyzes transaction patterns to identify and flag suspicious activity. The project includes data preprocessing, model training, and a user-friendly web interface for real-time fraud prediction.

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

This project uses predictive modeling techniques to identify fraudulent Credit Card transactions on data obtained from European credit card holders made in September 2013.

🔍 Predict fraudulent transactions with a pre-trained Random Forest Classifier model via Streamlit app.

💳 Creates a new gym environment for credit-card anomaly detection using Deep Q-Networks (DQN) and leverages Open AI's Gym toolkit to allocate appropriate awards to the RL agent.

Fraud Detection Data

This project aims to detect fraudulent transactions in the Ethereum blockchain using machine learning algorithms. Fraud detection is crucial for maintaining the integrity and security of the blockchain, and can help prevent financial losses due to fraudulent activity.

In this repo I have used SQL to analyze historical credit card transactions and consumption patterns in order to identify possible fraudulent transactions.

Detection of fraudulent transactions from IEEE Kaggle Dataset

Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.

Credit Card Fraud Detection