Heart Disease Risk Predictor

This project predicts the risk of heart disease based on various health parameters using machine learning.

Features

• Interactive web app built with Streamlit
• Machine learning model trained on heart disease dataset
• Real-time prediction with probability scores

Files

• Heart_Risk.ipynb: Jupyter notebook with data analysis, model training, and evaluation
• app.py: Streamlit web application for predictions
• heart.csv: Dataset used for training
• model.pkl: Trained machine learning model

Installation

1. Clone the repository:

   git clone https://github.com/Khushii-04/Heart_Risk_predictor.git
   cd Heart_Risk_predictor

2. Install dependencies:

   pip install streamlit pandas scikit-learn

Usage

Running the Web App

To run the Streamlit web application:

streamlit run app.py

This will start the app on http://localhost:8501. Open this URL in your browser to use the heart disease risk predictor.

Running the Notebook

To explore the data analysis and model training:

1. Open Heart_Risk.ipynb in Jupyter Notebook or VS Code
2. Run the cells to see the analysis and train the model

Model Details

The model is a Random Forest classifier trained on features like age, cholesterol, blood pressure, etc. It predicts the probability of heart disease risk.