Project Overview
Following the Wordle trend, instead of competing with my dad's impressive 3-attempt guess (and instead of studying for midterms!), I spent a weekend implementing an automated Wordle Bot while learning Python's web scraping capabilities with Selenium.
The bot uses letter frequency analysis, constraint satisfaction, and web automation to solve Wordle puzzles efficiently. It can handle edge cases like duplicate letters and recovers gracefully when guesses aren't in Wordle's word list.
First Guess Strategy
To find an optimal first guess, I analyzed letter frequencies in a filtered UK English dictionary of 5-letter words. The analysis revealed that the word "arose" contains the 5 most frequent letters, making it an excellent starting point.
Letter Frequency Analysis
Algorithm Features
🎯 Frequency Analysis
Optimal first guess based on letter frequency in English 5-letter words
🧩 Constraint Satisfaction
Intelligent filtering based on green, yellow, and black letter feedback
🔄 Error Recovery
Graceful handling when generated guesses aren't in Wordle's word list
🔍 Edge Case Handling
Proper treatment of duplicate letters with mixed feedback colors