Dataset_for_Analyzing_University_Students'_Behaviour

The dataset for behavior analysis of university students consists of 351 records and 51 questions, covering a range of factors that shed light on student demographics, academic performance, and social behavior. The mental health of a student can be scaled using this dataset. Key demographic variables include semester, age, height, weight, gender, religion, and family background. Academic performance is assessed through CGPA, daily study hours, and satisfaction with academic activities, while social behaviours include relationship status, social media usage, and smoking habits. The dataset also explores extracurricular involvement, career outlook, and perceptions of social value related to students' fields of study. Critical mental health indicators such as suicidal thoughts, sleep patterns, and psychological symptoms like anxiety, agitation, and stress are captured. Questions about satisfaction with sleep, daily functioning, and emotional well-being further contribute to understanding students' mental health. The mental health section is extensive, with multiple questions related to suicidal thoughts and behaviours. The dataset also covers sleep-related behaviors. Additionally, the dataset captures psychological symptoms through questions about difficulty winding down, experiencing breathing problems, lack of positive feelings, and difficulty relaxing. Indicators of anxiety, panic, and agitation, such as trembling, nervous energy, and overreaction to situations, are also recorded. The emotional state of the students is further explored, including feelings of worthlessness, a lack of enthusiasm, and feeling scared or that life is meaningless. This dataset provides a detailed and comprehensive foundation for behavior analysis of university students, enabling researchers to assess academic, social, and psychological factors that influence student well-being. It is well-suited for applying machine learning models to predict behavioural and mental health outcomes.