Hangman Game Development
Budget: ₹1,500 – ₹12,500 INR
Problem statement : "For this assignment, your mission is to write an algorithm that plays the game of Hangman.
When a user plays Hangman, the server first selects a secret word at random from a list. The server then returns a row of underscores (space separated)—one for each letter in the secret word—and asks the user to guess a letter. If the user guesses a letter that is in the word, the word is redisplayed with all instances of that letter shown in the correct positions, along with any letters correctly guessed on previous turns. If the letter does not appear in the word, the user is charged with an incorrect guess. The user keeps guessing letters until either (1) the user has correctly guessed all the letters in the word or (2) the user has made six incorrect guesses.
You are required to write a "guess" function that takes current word (with underscores) as input and returns a guess letter.
Your algorithm is permitted to use a training set of approximately 250,000 dictionary words. Your algorithm will be tested on an entirely disjoint set of 250,000 dictionary words. Please note that this means the words that you will ultimately be tested on do NOT appear in the dictionary that you are given. You are not permitted to use any dictionary other than the training dictionary we provided. This requirement will be strictly enforced by code review.
You are provided with a basic, working algorithm. This algorithm will match the provided masked string (e.g. a _ _ l e) to all possible words in the dictionary, tabulate the frequency of letters appearing in these possible words, and then guess the letter with the highest frequency of appearance that has not already been guessed. If there are no remaining words that match then it will default back to the character frequency distribution of the entire dictionary.
This benchmark strategy is successful approximately 18% of the time. Your task is to design an algorithm that significantly outperforms this benchmark."
I'm on the lookout for a ML Engineer to build a Hangman game.
- Need to use LSTM or BERT or other ML Modeling techniques
- Accuracy needs to be more than 75%
- Written explanation of the code
An ideal candidate will have a wealth of experience in ML, preferably with knowledge in Model building using State of the art techniques.
Will share complete details post discussion.
When a user plays Hangman, the server first selects a secret word at random from a list. The server then returns a row of underscores (space separated)—one for each letter in the secret word—and asks the user to guess a letter. If the user guesses a letter that is in the word, the word is redisplayed with all instances of that letter shown in the correct positions, along with any letters correctly guessed on previous turns. If the letter does not appear in the word, the user is charged with an incorrect guess. The user keeps guessing letters until either (1) the user has correctly guessed all the letters in the word or (2) the user has made six incorrect guesses.
You are required to write a "guess" function that takes current word (with underscores) as input and returns a guess letter.
Your algorithm is permitted to use a training set of approximately 250,000 dictionary words. Your algorithm will be tested on an entirely disjoint set of 250,000 dictionary words. Please note that this means the words that you will ultimately be tested on do NOT appear in the dictionary that you are given. You are not permitted to use any dictionary other than the training dictionary we provided. This requirement will be strictly enforced by code review.
You are provided with a basic, working algorithm. This algorithm will match the provided masked string (e.g. a _ _ l e) to all possible words in the dictionary, tabulate the frequency of letters appearing in these possible words, and then guess the letter with the highest frequency of appearance that has not already been guessed. If there are no remaining words that match then it will default back to the character frequency distribution of the entire dictionary.
This benchmark strategy is successful approximately 18% of the time. Your task is to design an algorithm that significantly outperforms this benchmark."
I'm on the lookout for a ML Engineer to build a Hangman game.
- Need to use LSTM or BERT or other ML Modeling techniques
- Accuracy needs to be more than 75%
- Written explanation of the code
An ideal candidate will have a wealth of experience in ML, preferably with knowledge in Model building using State of the art techniques.
Will share complete details post discussion.