Heart Attack Analysis and Prediction using Machine Learning with source code

 The project's goal is to create a prediction model that, using a variety of risk indicators, can forecast a person's likelihood of suffering a heart attack.

The project includes a dataset with 13 variables, including age, gender, the kind of chest discomfort, resting blood pressure, serum cholesterol levels, fasting blood sugar levels, and electrocardiographic findings. In order to create a machine learning model that can predict the chance of a heart attack, a dataset from 303 patients was collected.

To create the prediction model, the project makes use of a number of well-known machine learning methods, including logistic regression, decision trees, random forests, and support vector machines. Many performance indicators, including accuracy, precision, recall, and the F1 score, were used to assess the model's correctness.

The project also incorporates exploratory data analysis (EDA) to learn more about the dataset in addition to developing the prediction model. The EDA uses visuals to help users understand how the various aspects are distributed and how they relate to one another, including histograms, bar charts, and scatter plots.

The project offers a thorough description of the machine-learning-based heart attack analysis and prediction method. Anybody interested in learning about machine learning and data analysis in the healthcare industry should definitely check it out.

Source Code: https://github.com/shamimkhaled/Heart-Attack-Analysis-and-Machine-Learning