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.

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

Detection of fraudulent transactions from IEEE Kaggle Dataset

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.

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

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.

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

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

[Project Repository] Predicting fraudulent transactions.

💳 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.

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

Code to detect credit card fraud detecton

Credit Card Transactions Fraud Detection using Deep Learning.

Fraud transaction detection using Machine Learning algorithms on highly imbalanced dataset

Detect fraudulent transactions and accounts in a vast dataset

Data preprocessing and classification for the detection of fraudulent transactions

Catering to Blocker Fraud Company's expansion in Brazil, this data science and machine learning project focuses on detecting fraudulent financial transactions. Leveraging advanced analytics, it delivers insights into transaction legitimacy, aiding revenue generation and minimizing losses