Analysis of Gen Z Preferences in Improving English Proficiency using Machine Learning
Abstract
This study analyzes Generation Z (Gen Z) preferences in enhancing English proficiency using machine learning with the Random Forest algorithm. The analysis aims to determine how accurately Random Forest can predict or classify models. The dataset contains three classes: “Applications,” “Entertainment_Media,” and “Tutoring.” Data collected was processed during the pre-processing stage. With this dataset, the model was trained and identified the most influential features on model performance: factors, media, learning duration, understanding, and motivation. The model achieved an accuracy of 62% with hyperparameter tuning. This research aims to contribute to the development of more personalized learning methods for Gen Z.
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