Heart Disease Prediction Using Machine Learning

Project Overview

This project predicts the likelihood of heart disease using Machine Learning techniques based on patient clinical data.

Features

• Data preprocessing
• Exploratory Data Analysis (EDA)
• Logistic Regression model
• Model evaluation
• Streamlit web application

Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn
• Streamlit

Dataset

UCI Heart Disease Dataset

Model Used

Logistic Regression

How to Run

Install Dependencies

pip install -r requirements.txt

Run Streamlit App

cd app
py -m streamlit run app.py

Project Structure

Heart_Disease_Prediction/
│
├── data/
├── notebooks/
├── models/
├── app/
├── requirements.txt
└── README.md

Future Improvements

• Add more ML models
• Improve UI
• Deploy online
• Add probability prediction