Heart Disease Prediction Model

This project uses Logistic Regression to predict the likelihood of heart disease based on patient health indicators. It takes various features like age, cholesterol level, and chest pain type as inputs and predicts whether a person has heart disease.

Dataset

The dataset includes:

• Features like age, sex, cp (chest pain type), trestbps (resting blood pressure), chol (cholesterol), thalach (maximum heart rate achieved), exang (exercise-induced angina), and more.
• The target variable, target, where 1 represents a diseased heart and 0 represents a healthy heart.

Project Setup

1. Clone this repository:

   git clone https://github.com/ItzLabib/Heart-Disease-Prediction-using-Machine-Learning-with-Python.git
   cd heart-disease-prediction