Project Overview Game: Classic Hangman with intelligent adaptive features Tech Stack: HTML5, CSS3, JavaScript Live Demo: https://shreysherikar.github.io/RETRO-HANGMAN-95/ Retro Revival Implementation Classic Game Recreation ✅ Base Game: Traditional Hangman word-guessing mechanics Retro UI: Authentic Windows 95-style interface with CRT effects Nostalgic Elements: Neon colors, pixel fonts, and 90s aesthetics Modern AI Twist 🤖 Intelligent Word Selection: AI-driven difficulty progression based on player performance Smart Hint System: Context-aware hints that adapt to player skill level Dynamic Category Matching: AI selects optimal word categories based on success patterns Adaptive Scoring: Machine learning-inspired scoring that adjusts to player behavior

Technical Highlights Complex Logic Implementation State Management: Multi-layered game state with persistent player profiles Algorithm Design: Progressive difficulty engine with performance analytics Pattern Recognition: Player behavior analysis for personalized experience Data Structures: Efficient word database with categorized difficulty tiers

Key Features 6 Visual themes with authentic retro styling Smart category system (Animals, Nature, Technology, Fantasy) Performance tracking with adaptive difficulty Zero dependencies - pure vanilla JavaScript Learning Focus: Complex Logic This project demonstrates Complex Logic skills through: Adaptive AI Systems: Dynamic difficulty adjustment based on player performance State Management: Complex game state handling with persistence Algorithm Implementation: Smart word selection and hint generation Data Analysis: Player pattern recognition and behavior adaptation

Results Performance: <100KB, instant loading Compatibility: All modern browsers User Experience: Seamless retro gaming with intelligent features Code Quality: Clean, maintainable architecture

🚀 PLAY LIVE DEMO Successfully recreated classic Hangman with modern AI intelligence while maintaining authentic 90s retro aesthetics.