Hangman Algorithm Optimization Project

Objective: Improve a Hangman game-playing algorithm to significantly outperform an 18% success rate benchmark.
Key Details:

Task: Enhance the existing guess() function in Python that plays Hangman via API
Current Performance: Baseline algorithm wins ~18% of games
Goal: Achieve substantially higher win rate (targeting 60%+ would be competitive)
Constraints:

Must use only the provided 250k word training dictionary
Algorithm will be tested on completely different word set
6 wrong guesses allowed per game
Final evaluation on 1,000 games

What's Provided:

Working Python codebase with API integration
Training dictionary (250k words)
Basic frequency-based algorithm as starting point

Deliverables:

Enhanced guess() function implementation
Brief explanation of optimization strategy
Code should be ready for 1,000-game evaluation

Skills Needed:

Python programming
Algorithm optimization
Pattern recognition/NLP techniques
Understanding of probability/statistics

Timeline: urgent!

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