Research Article
Prediction of Wordle Results Based on Ridge Regression Model and K-means Clustering
@INPROCEEDINGS{10.4108/eai.26-5-2023.2334479, author={Xiuhan Zheng and Xiaoli Jiang and Xiaodong Fan and Xueshu Wu and Yue Zhou}, title={Prediction of Wordle Results Based on Ridge Regression Model and K-means Clustering}, proceedings={Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26--28, 2023, Nanjing, China}, publisher={EAI}, proceedings_a={MSEA}, year={2023}, month={7}, keywords={time series model ridge regression k-means clustering the word game}, doi={10.4108/eai.26-5-2023.2334479} }- Xiuhan Zheng
Xiaoli Jiang
Xiaodong Fan
Xueshu Wu
Yue Zhou
Year: 2023
Prediction of Wordle Results Based on Ridge Regression Model and K-means Clustering
MSEA
EAI
DOI: 10.4108/eai.26-5-2023.2334479
Abstract
Wordle is a word game that became known in January 2022. In order to innovate the game and improve the participation of the game, this paper analyzes a series of data by using time series model, gray prediction model, ridge regression, K-means clustering and other methods. In this paper, we predict the effects of player number intervals and word attributes on the results of Wordle at a certain stage in the future, and classify the words to find out the characteristics of each class of words, and predict the reported results of a certain word based on the word characteristics. At the same time, our experience can be used as classroom examples of innovative word game strategy algorithms and statistics to demonstrate the research of this paper.