Spam Message Detection
Spam Message Detection
Machine Learning-Based Text Classification System
Editorial Case Review
This machine learning tool classifies short text communications as legitimate (ham) or spam with high statistical confidence. Built with a robust pipeline containing text tokenizers, stop-word filters, and feature extraction components, the application processes messages instantly, generating predictive diagnostics for fraud detection.
Core Capabilities
Text preprocessing using NLP techniques
Trained and evaluated ML models for spam classification
Real-time message prediction with probability scoring
Engineering Case Study
Handling adversarial spelling adaptations, character substitution tricks (like using slashes/symbols for words), and lexical variance in brief SMS content.
Designed a clean preprocessing wrapper utilizing NLTK for stem extraction and tokenization, and vectorizing strings with TF-IDF. The system combines Multinomial Naive Bayes and Linear SVM models for optimal results.
Development Journey & Milestones
Architecture & System Design
Structured the schema for Spam Message Detection, defining state management patterns and API route handlers.
Full-Stack Implementation
Engineered core features using Python, Scikit-learn, Pandas with component modularity.
Performance & Optimization
Applied server-side rendering, debounced event flows, and asset compression for top Lighthouse scores.
Deployment & CI/CD Telemetry
Deployed live production build with continuous integration and automated telemetry tracking.
Key Specifications
Built With
Product Performance
Local Setup Commands
git clone https://github.com/Rameshwar-bhagwat10/portfolio.git
cd spam-message-detection
npm install
npm run devInterface Gallery