A Word's Difficulty Level Classification Model Based on Random Forest Algorithm and K-Means Clustering Algorithm

In recent years, COVID-19 has brought a huge impact on people’s life, making online activities a new trend, which promotes the rapid development of mini-games. A mini-game called "Wordle" came out currently has been brought into focus. Players are required to solve the puzzle by guessing a five-letter word in six tries or less, receiving feedback with every guess. We use machine learning algorithms, random forests and K-means clustering respectively to classify solution word (EERIE as an example) by difficulty based on previous data. According to our model, "EERIE" is verified to belong to the medium difficulty category in both algorithms. Specially, we find that compared with K-means clustering algorithm, random forest algorithm has a higher accuracy in word difficulty classification.