Insight into wordle's data set based on deep learning

Abstract

Nowadays, Wordle became almost everyone's current obsession. To study the reason for Wordle’s explosion, look for the secret behind Wordle. It is beneficial to develop a forecasting model to measure the fluctuations and distributions of the results based on time series and words. In the text used the context processing of words in text sequences in natural language processing to analogize that the same rule can be used for the composition and structure of words, so as to establish a percentage prediction model for the number of attempts of players with the character mechanism of letter position and structure in words. The error uncertainty of the model is evaluated by the MAPE error value. Through the analysis of the MAPE value, the error of the model to the predicted value is about 1.92%, so it is confident that the model can complete the prediction task with an error not exceeding 1.92%. Through this model, Predicting the result of the word "EERIE" as (2.16, 10.90 14.06, 24.49, 25.79, 14.41, 3.45).