Sleep plays an important role in people’s quality of life, and its deprivation can lead to disorders such as insomnia and sleep apnea. In this study, we evaluate a dataset related to sleep quality from 374 users and use it to compare classification models with hyperparameter tuning. The cross-validation technique was employed in order to obtain the best accuracy, F1-score, precision and recall metrics. The results show that the Random Forest model is the most promising for this dataset, achieving an accuracy and F1-score values of approximately 92% and 90%, respectively.