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.

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

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

Data preprocessing and classification for the detection of fraudulent transactions

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

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

The visual graph of fraud detection website

Production MLOps pipeline for fraud detection with automated testing, monitoring, and zero-downtime deployments

🎯 A comprehensive Bank Identification Number (BIN) intelligence platform for e-commerce fraud detection and prevention.

A book project accompanying the CopyDetect package. The book provides comprehensive coverage of response similarity analysis using R.

🧠 ML training pipeline within our MLOps architecture. Manages the training, evaluation, and versioning of the CatBoost model.

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

This project is an Insurance Workflow Management System designed to streamline policy management, claims processing, and fraud detection. It includes user account management, customer feedback analysis via NLP, alert notifications through SMS, and a fraud detection model, providing a secure, efficient solution for insurance operations.

🚫 Detect ad fraud easily with AdTruth, the open-source solution designed to help small businesses grow through genuine traffic.

Team project for BA810 (Supervised Machine Learning)

Machine Learning project to detect fraudulent transactions on ethereum blockchain, reducing false positives

I created a website using Flask framework for detecting financial fraud using Machine Learning Model

Machine learning model to detect fraudulent mass subscriptions by analyzing user behavior patterns like tab switching, typing speed, and navigation activity.

ML-powered FastAPI system for detecting fake product reviews with sentiment analysis