Heart Disease Prediction App

Overview

This project is a Streamlit-based web application for predicting heart disease severity using a pre-trained machine learning model. The model takes various patient health metrics as input and provides a prediction on the likelihood of heart disease.

Features

• User-friendly web interface built with Streamlit
• Accepts patient details such as age, cholesterol level, heart rate, and more
• Uses a pre-trained machine learning model to predict heart disease severity
• Displays prediction results in an easy-to-understand format

Files in This Repository

• app.py - Main Streamlit application script
• heart_disease_pred_model.pkl - Pre-trained machine learning model
• heart_disease_data.csv - Dataset used for training the model
• Heart_Disease_Prediction.ipynb - Jupyter notebook containing data preprocessing, model training, and evaluation

Installation

To run the application locally, follow these steps:

1. Clone this repository:

   git clone https://github.com/hamzakamelen/Heart-Disease-Prediction.git
   cd Heart-Disease-Prediction

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the application:

   streamlit run app.py

Usage

1. Open the app in your browser after running streamlit run app.py
2. Enter the patient's health details in the provided fields
3. Click the "Predict Heart Disease Severity" button
4. View the model's prediction for heart disease severity

Contributing

Feel free to contribute to this project by submitting issues or pull requests.

License

This project is licensed under the MIT License.

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Developed by Hamza Kamelen