CEREBRAL-VASCULAR ACCIDENT DETECTION USING SOFT VOTING This project aims to develop a robust brain stroke detection model using a soft voting ensemble technique, integrating multiple machine learning algorithms to enhance predictive performance. Given the high morbidity and mortality associated with strokes, timely and precise diagnosis is crucial for effective treatment. Traditional methods often fall short in speed and accuracy, necessitating reliable automated tools. The project involves data collection and preprocessing from diverse medical datasets, training individual models like logistic regression, decision trees, and support vector machines, and combining them through soft voting to improve accuracy and robustness. Performance evaluation will utilize metrics such as accuracy, precision, recall, F1-score, and AUC ROC, with cross-validation ensuring reliability. The final model will be deployed in a user-friendly interface for real-time clinical application, aiding in early stroke detection. Ethical considerations, including data privacy and bias mitigation, will be integral throughout the project. This advanced detection tool aims to enhance early stroke diagnosis, improve patient outcomes, and reduce healthcare burdens, with provisions for future enhancements and broader clinical adoption. By leveraging a soft voting ensemble technique, this project seeks to develop a highly accurate and reliable brain stroke detection model. The integration of multiple machine learning algorithms aims to overcome the limitations of traditional methods, offering a powerful tool for early and precise stroke diagnosis. This initiative not only promises to enhance patient care but also aims to pave the way for future advancements in medical diagnostic