Student stress analysis using machine learning

The Student Stress Analysis idea employs a di-verse set of machine learning algorithms, including Random Forest, Decision Tree, K-Nearest Neighbors (KNN), and Natural Language Processing (NLP) techniques, to explore and analyze the intricate web of stressors affecting students. This research embraces a holistic approach, delving into academic, social, and emotional domains to cultivate a nuanced understanding of stress triggers. The integration of a sophisticated Chabot system further enhances the framework, providing personalized support mechanisms. Leveraging AI technologies, this system offers tailored strategies to help students navigate the complexities of academic life. The project is committed to improving student well-being and academic success, aiming to empower individuals by providing invaluable insights, resources, and interventions. Through collaborative efforts and innovative solutions, the initiative seeks to foster a supportive environment conducive to student flourishing and resilience.