karanwxliaa/Texquant_Hangman

Hangman Game Solver

This project provides an automated solution to play the Hangman game by leveraging data analysis and pattern recognition to make educated guesses. It analyzes a large dataset of English words to understand their characteristics and uses this information to improve guess accuracy during the game.

Overview

The solver begins by analyzing a dataset of 250,000 English words, examining word lengths, vowel/consonant distributions, and creating a frequency distribution of letters and n-grams (substrings). This analysis informs the solver's strategy when playing Hangman, allowing it to make guesses based on statistical insights derived from the dataset.

Methodology

The project is divided into two main components: Data Analysis and Game Logic.

Data Analysis

1. Word Length Distribution: Analyzes and visualizes the distribution of word lengths.
2. Vowel and Consonant Frequency: Calculates the total and average number of vowels and consonants per word.
3. Average Vowels per Word Length: Computes the average number of vowels for words of each length.

Game Logic

1. Hangman API Integration: Communicates with a Hangman game server to start games and make guesses.
2. Dictionary Preparation: Loads a dictionary of words and analyzes it to prepare for the game.
3. N-Word Dictionary: Creates a dictionary of n-grams to help with pattern matching.
4. Guessing Strategy: Uses regex matching and statistical analysis to make educated guesses.

Flowchart

flowchart TB
    A[Start] --> B{Analyze Word List}
    B --> C[Word Length Distribution]
    B --> D[Vowel/Consonant Counts]
    B --> E[Avg. Vowels per Word Length]
    C --> F[Load Hangman Game]
    D --> F
    E --> F
    F --> G{Make Guess}
    G -->|Match| H[Find Matching Words]
    G -->|No Match| I[Use Common Letters]
    H --> J[Guess Based on Frequency]
    I --> J
    J --> K{Check Game Status}
    K -->|Win| L[End Game]
    K -->|Lose| L
    K -->|Ongoing| G
    L --> M[Report Results]

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