Stroke Prediction Using ML

This project aims to build a predictive model that assesses the risk of stroke in individuals based on various health indicators. By leveraging machine learning techniques, the model analyzes features such as age, gender, hypertension, heart disease, glucose level, BMI, and smoking status to predict the likelihood of a stroke. The project includes data cleaning, exploratory data analysis, and training classification algorithms to achieve accurate predictions. Such a system can support early detection and preventative healthcare decisions, potentially saving lives through timely interventions.