#  Heart Health Navigator: AI-Powered Heart Disease Prediction Web App

An interactive web application built with **Streamlit** that uses a **Random Forest** machine learning model to predict the risk of heart disease based on key medical attributes.

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## 📝 Project Description

The **Heart Health Navigator** is a comprehensive, user-friendly tool designed to provide data-driven insights into cardiovascular health. It features:

- Secure **user authentication**.
- Educational sections with **interactive data visualizations**.
- An AI-powered **prediction tool**.
- A downloadable **summary report** for each prediction.

This project demonstrates a complete **end-to-end machine learning workflow**, from model training to deployment as an interactive web application.

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## ✨ Key Features

- **User Authentication:** Secure Sign Up and Login system.
- **Multi-Page Interface:** Easy navigation via top menu bar.
- **Interactive Prediction Tool:** Input 13 key medical parameters for accurate risk assessment.
- **Data Visualization:** Explore heart disease datasets using interactive charts with Plotly.
- **Downloadable Reports:** Download prediction summaries as PDF reports.
- **Session History:** Keep a log of recent predictions and download as CSV.

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## 💻 Tech Stack

- **Programming Language:** Python
- **Web Framework:** Streamlit
- **Machine Learning:** scikit-learn (Random Forest)
- **Data Manipulation:** pandas
- **Model Persistence:** joblib
- **Data Visualization:** plotly
- **PDF Generation:** FPDF2
- **UI Components:** streamlit-option-menu

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## 🚀 Getting Started

Follow these steps to set up and run the project locally.

### Prerequisites

- Python 3.9+
- pip (Python package installer)

### Installation & Setup

1. **Clone the repository:**
```bash
git clone https://github.com/your-username/heart-health-navigator.git
cd heart-health-navigator

2. Install dependencies: It's recommended to use a virtual environment.

pip install -r requirements.txt

3. Ensure the following files are present in the main project directory:

   • heart.csv (dataset)
   • heart_disease_model.joblib (trained model)
   • scaler.joblib (trained scaler)

4. User Database: On the first run, the app will automatically create a users.csv file:

username,password,email

Running the Application

streamlit run app.py

The app will open in your default web browser.

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📁 Project Structure

.
├── .streamlit/
│   └── config.toml
├── app.py
├── heart.csv
├── heart_disease_model.joblib
├── scaler.joblib
├── users.csv
└── requirements.txt

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👤 Author

Mannaswini P A

• 📧 Email: [email protected]
• LinkedIn: linkedin.com/in/mannaswini-p-a
• GitHub: @imannaswini

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📝 requirements.txt

streamlit
pandas
scikit-learn
joblib
plotly
fpdf2
streamlit-option-menu

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