Census Income Integration

📌 Overview

This project integrates with Census.gov API to fetch and display regional income data, allowing users to compare their income with the median regional income.

🚀 Live Demo

🔗 Census Income App

📂 Repository

🔗 GitHub Repository

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📊 Features Implemented

🔹 1. API Integration

• Fetches real-time regional income data from Census.gov.
• Handles API failures and incomplete datasets with proper error messages.
• Normalizes data for consistent display.

🔹 2. User Flow

1️⃣ User inputs or confirms location (auto-detection enabled).2️⃣ System fetches & displays regional median income.3️⃣ User’s income is ranked as a percentage, alongside a bar chart comparison.

🔹 3. User Interface (UI)

• Displays user’s income ranking as a percentage of regional income.
• Simple bar chart visualization for comparison.
• Fully responsive design for mobile and desktop.

🔹 4. Error Handling & Fallbacks

• Displays an error message if Census data is unavailable.
• Allows users to manually input location if auto-detection fails.
• Provides graceful fallback handling in case of API failures.

🔹 5. Performance & Scalability

• Optimized API calls for fast response times.
• Ensures scalability for multiple users accessing the system simultaneously.
• Maintains 99% data integration accuracy.

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🛠️ Setup & Installation

1. Clone the Repository

git clone https://github.com/Jamilbilal47/census-income.git
cd census-income

2. Install Dependencies

npm install

3. Create an .env.local File (if required)

• Add API keys or any required environment variables.

4. Run Locally

npm run dev

The app should be running at http://localhost:3000.

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📦 Deployment

The project is deployed on Railway and can be accessed here:🔗 Live Deployment

For deployment:

• Railway automatically builds and deploys from GitHub.
• The build command is: npm run build
• The start command is: npm start

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❗ Challenges & Trade-offs

• Handling API rate limits: Implemented error handling for cases where API responses are delayed.
• Location auto-detection fallback: Users can manually enter their location if auto-detection fails.
• Performance considerations: Ensured optimized API calls to reduce load times.

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✨ Future Enhancements

• Add multi-region comparisons.
• Implement historical income trends visualization.
• Enhance UI design with interactive elements.

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💍 Contact

For any queries, feel free to reach out:🌟 Jamil Bilal | 📧 [email protected]

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This README.md provides a clear project overview, setup instructions, features, challenges, and future enhancements. Let me know if you need any modifications! 🚀