Scanner, signatures and the largest collection of Magento malware

🔍 | 📈 | Life / Health Insurance Fraud Detection | 📋 | (Codeshahstra Round 1 Hackathon)

This research goal is to build binary classifier model which are able to separate fraud transactions from non-fraud transactions.

MER is a software that identifies and highlights manipulative communication in text from human conversations and AI-generated responses. MER benchmarks language models for manipulative expressions, fostering development of transparency and safety in AI. It also supports manipulation victims by detecting manipulative patterns in human communication.

The objective of this project is to explore and learn various Machine Learning Algorithm and see how it solves different Business problems. There are various models like Decision tree, Random Forest, Naive Bayes Classifier, linear regression, Logistic regression etc.

Team project for BA810 (Supervised Machine Learning)

Report on the performance of different machine learning algorithms in identifying persons of interest in the Enron Fraud Case

This solution performs Anomaly Detection with Statistical Modeling on Spark. The detection is based on Z-Score calculated on cpu usage data collected from servers.

This project uses machine learning models like Logistic Regression, Random Forest, and XGBoost to detect fraudulent credit card transactions. It handles class imbalance using SMOTE and visualizes key fraud patterns through an interactive Power BI dashboard.

Fraud Detection Research - Data Science Capstone Project at Penn State University, University Park Campus

The goal of the competition was to predict fraudulent transactions on a dataset with about 40 million instances, with some characteristics similar to the datasets processed by Feedzai.

Fraud Detection of a 6 million row dataset using AWS and Spark

Fraud Detection Case Study

This project implements an end-to-end pipeline for detecting SMS spam using LLM-based embeddings (Mistral), interpretable machine learning, and risk-aware reporting.

This project is a credit card fraud detection system using machine learning and speech recognition to identify fraudulent transactions. It employs a Support Vector Machine (SVM) model to classify transaction types based on clues provided via speech inputs.

Detecting fraud on online customer transactions

Data preprocessing and classification for the detection of fraudulent transactions

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

Machine learning models for credit card fraud detection with baseline vs SMOTE comparison, evaluated using Recall, Precision, and F1-score.