Spam-detection

bySasidhar

Problem Statement With the rapid growth of digital communication through emails, SMS, and messaging platforms, users are increasingly exposed to spam messages, phishing attempts, fraudulent links, and unwanted advertisements. These spam messages waste time, reduce productivity, consume storage resources, and can lead to financial fraud or data theft. Traditional rule-based filtering systems often fail to detect modern spam patterns because spammers continuously change message structures, keywords, and writing styles. Therefore, there is a need for an intelligent, scalable, and automated system that can accurately identify spam messages in real time. The proposed system aims to build an AI-powered spam detection platform using Machine Learning and Natural Language Processing (NLP) techniques to automatically classify incoming messages as Spam or Not Spam (Ham). The system will analyze message content, extract meaningful features, and provide fast and accurate predictions through a web-based interface and backend API. The application should support: Real-time spam prediction High classification accuracy User-friendly interface Scalable backend architecture API-based integration for future platforms The system can be further extended to detect phishing URLs, malicious content, scam messages, and multilingual spam detection. Frontend: React + TypeScript + Tailwind Backend: Node.js + Express + TypeScript ML Service: Python (separate service) Database: PostgreSQL