UX/UI - Companion App Using Data Science

Heart disease and stroke are on the rise worldwide, even among children. To avoid early stroke, the prediction process must be completed for early detection of stroke symptoms. Predicting stroke is a complex task that requires detailed prior knowledge. The plan is to predict heart attacks using data from Kaggle. A predictive model estimates a person's stroke risk based on factors such as age, gender, diabetes, smoking, body weight, occupation, and location. It classifies a person's risk using various machine learning techniques are Random Forest, Decision Trees, Naive Bayes, Support Vector Machines(SVM), Logistic Regression, and K Nearest Neighbors (KNN) were employed and assessed to identify the most effective algorithm. Upon analysis, the Decision Trees algorithm emerged as the optimal method, exhibiting unparalleled accuracy at a rate of 100%.