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.

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.

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

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.

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

An AI-powered fraud detection system that uses machine learning to detect suspicious financial transactions in real time. Features include interactive dashboards, secure authentication, and comprehensive reporting for fintech risk analysis.

A deep learning-based web application for deepfake video detection, powered by the fine-tuned XceptionNet (Extreme Inception) model. The system allows users to upload videos for deepfake detection, processes them through the trained model, and provides results via a clean Django-based web interface.

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

Test repo for the Smartboard project

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.

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

The objective of this project is to develop a robust classification model capable of identifying and flagging potentially fraudulent job postings on LinkedIn.

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