The Heart Disease Prediction Model, employing Logistic Regression, is designed to assess the likelihood of an individual having heart disease based on crucial parameters. Utilizing factors like age, blood pressure, cholesterol levels, and exercise induced angima, the model evaluates the probability of an individual having a heart condition. Logistic Regression, a binary classification algorithm, efficiently models the relationship between these factors and the presence or absence of heart disease. By analyzing a dataset containing historical health records and corresponding health conditions, the model learns to predict the probability of heart disease occurrence in new cases.