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Life Expectancy Prediction - Web Application

A modern web application that predicts life expectancy based on lifestyle and health factors using machine learning.

🚀 Quick Start

Option 1: Docker (Recommended) 🐳

Prerequisites: Docker and Docker Compose

# Build and start the application
docker-compose up -d

Visit:

Option 2: Manual Setup

Prerequisites: Python 3.8+, Node.js 18+, npm

Backend Setup

  1. Navigate to the backend directory:
cd backend
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
  1. Start the Flask server:
python app.py

The API will run on http://localhost:5000

Frontend Setup

  1. Navigate to the frontend directory:
cd frontend
  1. Install dependencies:
npm install
  1. Start the development server:
npm run dev

The app will run on http://localhost:5173

📁 Project Structure

healthy-life-webapp/
├── backend/                  # Flask API
│   ├── app.py               # Main Flask application
│   ├── requirements.txt     # Python dependencies
│   ├── model/              # Trained ML model files
│   │   ├── model.pkl
│   │   ├── scaler.pkl
│   │   ├── feature_info.json
│   │   └── top_features.json
│   └── utils/              # Utility modules
│       ├── converter.py    # Feature conversion
│       └── predictor.py    # Prediction logic
│
└── frontend/               # React + TypeScript
    ├── src/
    │   ├── components/    # React components
    │   │   ├── Welcome.tsx
    │   │   ├── QuestionForm.tsx
    │   │   └── ResultDisplay.tsx
    │   ├── services/      # API service
    │   │   └── api.ts
    │   ├── types/         # TypeScript types
    │   │   └── index.ts
    │   ├── App.tsx        # Main app component
    │   └── main.tsx       # Entry point
    ├── package.json
    └── tailwind.config.js

🎯 Features

  • Interactive Questionnaire: 10 simple questions about lifestyle and health
  • Real-time Prediction: Instant life expectancy calculation using XGBoost ML model
  • Visual Results: Beautiful charts and cards showing predicted life expectancy
  • Top 10 Factors: Identifies which factors positively or negatively impact your life expectancy
  • Personalized Recommendations: Actionable advice to improve health outcomes
  • Responsive Design: Works perfectly on desktop, tablet, and mobile devices

🛠️ Technology Stack

Backend

  • Flask: Lightweight Python web framework
  • XGBoost: Machine learning model for predictions
  • scikit-learn: Data preprocessing and scaling
  • Flask-CORS: Cross-origin resource sharing

Frontend

  • React 18: UI library
  • TypeScript: Type-safe JavaScript
  • Vite: Fast build tool and dev server
  • Tailwind CSS: Utility-first CSS framework
  • Axios: HTTP client for API calls
  • Recharts: Data visualization library

📊 How It Works

  1. User Input: User answers 10 questions about their lifestyle, health, and demographics
  2. Feature Conversion: Responses are converted to 34 model features using population statistics
  3. ML Prediction: XGBoost model predicts life expectancy based on features
  4. Factor Analysis: Top 10 influential factors are identified and explained
  5. Results Display: User sees their predicted life expectancy, comparison to average, and personalized recommendations

🔒 Privacy

  • All data is processed in real-time
  • No personal information is stored on servers
  • Calculations happen locally or in temporary API calls
  • No tracking or analytics

🚦 API Endpoints

  • GET / - API information
  • GET /api/health - Health check
  • GET /api/questions - Get questionnaire questions
  • POST /api/predict - Submit data and get prediction

🧪 Development

Run Backend in Development Mode

cd backend
export FLASK_ENV=development
python app.py

Run Frontend in Development Mode

cd frontend
npm run dev

Build Frontend for Production

cd frontend
npm run build