Heart Attack ✔️Analysis Data

The heart attack dataset is a collection of data that can be used to predict the risk of heart attack in a patient. The dataset contains 14 features, including age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise induced angina, oldpeak, slope, number of major vessels colored by flourosopy, thal, and target. The target variable is a binary variable that indicates whether the patient had a heart attack (1) or not (0).

The dataset was originally collected from the Cleveland Clinic Foundation and has been used in many research studies to develop models for predicting heart attack risk. In one study, researchers used the dataset to develop a model that was able to predict heart attack risk with an accuracy of 85%. The heart attack dataset is a valuable resource for researchers and clinicians who are interested in developing models for predicting heart attack risk. The dataset is freely available online and can be used to train and evaluate machine learning models.