Karakter özellikleri ve sensör tabanlı günlük aktivite verisi kullanılarak öznel iyi oluş tahmini

People enjoy different things in life. For example, while some people enjoy walking and socializing, others may get the same pleasure from sleeping. Enjoyment improves people's moods, and different metrics are used in psychology to measure that well-being level. One of the most widely used is the "subjective well-being" scale. The degree of this scale is measured by short questionnaires applied to individuals. In this study, subjective well-being was chosen as the target. We transformed the target to a binary scale (0-1), and the problem is considered as classification. In the model created, the individuals' subjective well-being was estimated by applying Decision Tree, Logistic Regression, Naïve Bayes, SVM, KNN and Ensemble classification methods. The person's daily physical activity, socialization, and big five personality traits were used as attributes. As a result of the models applied, we have achieved an accuracy between 64 and 80%. Unlike other studies, this study revealed a model that predicts 80% accuracy by composing sensor data with character traits. In this study, NetHealth dataset that collected from college students during consecutive periods was used.