<![CDATA[This study aims to analyse the relationship between individuals' sleep health and lifestyle using machine learning algorithms. The Sleep Health and Lifestyle dataset used in the study includes variables such as age, gender, occupation, physical activity, stress level, and sleep duration. The data has been cleaned during the pre-processing stage and normalisation procedures have been applied. Subsequently, the classification of individuals' sleep quality was performed using the K-Nearest Neighbour (KNN), Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) algorithms. Model performance has been evaluated using metrics such as accuracy, F1-score, precision and sensitivity. In this study, the 5-fold cross-validation method was preferred to evaluate the model's performance in a more reliable and generalisable manner. The results show that ANN and Random Forest models achieve a higher accuracy rate compared to other algorithms. These findings reveal that lifestyle factors have a strong influence on predicting sleep quality.]]>
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