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.
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