Scanner, signatures and the largest collection of Magento malware

Open-source ad fraud detection for small businesses using machine learning. Detect click fraud and bot traffic from Google Ads, Facebook Ads, and other platforms. Completely free.

🔍 | 📈 | 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)

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.

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.

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 Case Study

Iraboti 🌐 Transparent Lottery Software 🌐 Choose winners with confidence using this open-source, tamper-proof lottery software 🌐 Live Preview at https://tawhidurrahmandear.github.io/iraboti

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.

Built an unsupervised Machine Learning pipeline to detect anomalies in Bitcoin transactions by selecting 19 key features from 700.

A machine learning-based web application to detect financial fraud in real time. Users can input transaction details and get instant fraud predictions.

A sample website integrating the ComplyCube SDK.

A collection of projects where I worked on building anomaly detection pipelines. This rep covers code for EDA, outlier detection, and stock analysis.