Heart Attack Prediction

Heart Attack Prediction – Project Description

This project is a machine learning model designed to predict the likelihood of heart attacks based on patient health data.

Using Python and libraries such as scikit-learn, pandas, and NumPy, the model analyzes key medical indicators (like age, cholesterol level, blood pressure, etc.) to classify whether a person is at risk.

Key Features:

Cleaning and preprocessing medical datasets.

Training and testing classification models (Logistic Regression, Random Forest, etc.).

Evaluating performance with metrics such as accuracy, precision, and recall.

Providing clear Python code and visualizations of the results.

Outcome:

A practical tool that demonstrates how machine learning can assist in early detection of heart attack risks and support decision-making in healthcare.