🫀 Heart Disease Prediction using Machine Learning

📌 Overview

This project predicts whether a person is at risk of heart disease based on clinical features such as age, cholesterol level, blood pressure, etc. It uses Machine Learning models (e.g., Logistic Regression, Decision Tree, Random Forest, etc.) to classify patients as having heart disease (1) or not (0).

📂 Dataset

Dataset used: [UCI Heart Disease Dataset](https://www.kaggle.com/datasets/redwankarimsony/heart-disease-data)

(or mention Kaggle link if you used it).

Features include:

• Age

• Sex

• Chest Pain Type

• Resting Blood Pressure

• Cholesterol

• Fasting Blood Sugar

• Maximum Heart Rate Achieved

… (list major ones).

⚙️ Tech Stack

• Programming Language: Python 🐍

Libraries:

• pandas, numpy, matplotlib, seaborn (data analysis & visualization)

• scikit-learn (ML models)

• Jupyter Notebook (experimentation)