Mental health illness prediction system using machine learning algorithms focuses on the use of artificial intelligence techniques to support early detection of mental health conditions. Here, students are considered as target population. This book explores how machine learning algorithms can be effectively applied to predictive models of mental health illnesses to predict any kind of disorder among students so that early precaution can be taken. It presents a structured framework for designing and implementing an intelligent prediction system based on machine learning, covering data preparation includes data collection, data preprocessing, feature extraction, algorithm selection techniques for model development, training, testing, and system deployment, performance analysis. In addition to theoretical insights, the book presents real-world case studies that illustrate the deployment of predictive models in mental health applications. It highlights various machine learning algorithms and their role in improving prediction accuracy and decision support. The content addresses real-world challenges for students, developers, and healthcare researchers and professional audiences, the book bridges the gap between mental health studies and intelligent computing technologies. It supports early diagnosis and decision-making in mental health care.

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