MindKDD:KDD Predictor

Mental health is a fundamental component of overall well-being; however, it is frequently neglected, particularly among individuals affected by substance use. The increasing prevalence of mental health disorders such as depression, anxiety, and stress highlights the urgent need for accessible, stigma-free, and technology-driven solutions that promote early detection and awareness. Many substance users fail to recognize the warning signs of mental distress or hesitate to seek professional help due to fear of judgment, social stigma, or limited access to healthcare services. Traditional mental health assessment methods rely heavily on manual clinical evaluations, which are often time-consuming, expensive, and not readily available to everyone. These challenges emphasize the necessity for an intelligent system capable of identifying risks at an early stage and encouraging preventive care.The project “MindKDD – KDD Predictor” is Machine Learning-based platform developed to identify early signs of mental health issues among substance users. It analyzes structured questionnaires and behavioral data to predict potential mental health risks. The system utilizes machine learning algorithms such as Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and a Voting Classifier to improve prediction accuracy and classify users into high-risk or low-risk categories.The platform aims to provide a private, secure, and user-friendly self-assessment environment where individuals can evaluate their mental well-being anonymously. This encourages honest responses and enhances prediction reliability. After analysis, the system offers personalized recommendations, coping strategies, and awareness content, enabling users to take proactive steps toward better mental health