Eco-Guard: AI-Powered Air Quality Monitoring Platform

Table of Contents

• Introduction
• Features
• Technologies Used
• Installation
• Usage
• API Endpoints
• Contributing
• License

Introduction

Eco-Guard is a web-based AI-powered air quality monitoring platform designed to provide real-time air quality data, predictive analytics, and personalized recommendations. The platform uses machine learning models to forecast air quality and offers an interactive user experience through responsive web design.

Features

• Real-Time Monitoring: Access real-time air quality data for various districts.
• Predictive Analytics: Forecast air quality for the upcoming week using LSTM (Long Short-Term Memory) models.
• Interactive Maps: Visualize air quality data across different locations.
• Location-Specific Alerts: Receive alerts based on your location and current air quality conditions.
• Public Health Recommendations: Get tailored health recommendations based on air quality levels.
• Data Integration: Integrate data from environmental agencies and weather stations.

Technologies Used

• Frontend: React
• Backend: Node.js, Express
• Database: MongoDB
• Machine Learning: TensorFlow/Keras for LSTM models
• Others: Flask (for Python integration), dotenv, and more.

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/AnwitDahal/Eco-Guard.git
   cd Eco-Guard

2. Install dependencies:

   npm install

3. Set up environment variables: Create a .env file in the root directory and add the necessary environment variables as specified in .env.example.

4. Start the development server:

   npm start

Contributing

We welcome contributions to Eco-Guard! To contribute, follow these steps:

1. Fork the repository: Click the "Fork" button at the top right of the repository page.

2. Clone your forked repository:

   git clone https://github.com/your-username/Eco-Guard.git
   cd Eco-Guard

3. Create a new branch:

   git checkout -b feature-name

4. Make your changes and commit them:

   git commit -m "Description of your changes"

5. Push to your forked repository:

   git push origin feature-name

6. Create a pull request: Go to the original repository and click the "New Pull Request" button.

License

This project is licensed under the MIT License. See the LICENSE file for details.