This is a simple Streamlit web application that allows users to predict the likelihood of heart disease based on input features. The prediction is made using a machine learning model that has been trained on heart disease data.

Live Website

• Getting Started
  • Prerequisites
  • Installation
• Usage
• Input Features
• Output

To run this application, you need:

• Python (3.6 or later)
• Streamlit
• pandas
• pickle

1. Clone this repository:

git clone https://github.com/Prem07a/Heart-Disease.git

2. Navigate to the project directory:

cd Heart-Disease

3. Install the required dependencies:

pip install -r requirements.txt

Run the Streamlit app by executing the following command in your terminal:

streamlit run ./code/website/app.py

The web application will open in your default web browser. You can then interact with the app by inputting various features and clicking the "Predict" button to get the predicted outcome.

The following input features can be adjusted in the app:

• Age
• Sex (Male/Female)
• Chest Pain Type
• Resting Blood Pressure
• Cholesterol
• Fasting Blood Sugar (> 120 mg/dl)
• Resting Electrocardiographic Results
• Maximum Heart Rate Achieved
• Exercise Induced Angina
• ST Depression Induced by Exercise Relative to Rest
• Slope of the Peak Exercise ST Segment
• Number of Major Vessels Colored by Fluoroscopy
• Thalassemia

After adjusting the input features and clicking "Predict," the app will display the predicted outcome, indicating whether the prediction suggests a positive or negative likelihood of heart disease.

I would like to thank Akshat for reporting a bug in streamlit app.