Air Quality Forecasting and Analysis

This project provides a full-stack solution for forecasting and analyzing air quality data. It includes a Python backend for data processing and modeling, and a React frontend for data visualization and user interaction.

Features

• Air Quality Forecasting: Predicts future air quality levels for various pollutants.
• Data Visualization: Interactive charts and heatmaps to visualize historical and forecasted data.
• Health Recommendations: Provides personalized health advice based on AQI levels.
• Crowdsourced Feedback: Allows users to submit feedback on current air quality conditions.
• Model Performance Metrics: Displays metrics to evaluate the accuracy of the forecasting models.

Technologies Used

Backend

• Python: The core language for data processing and modeling.
• FastAPI: A modern, fast (high-performance) web framework for building APIs.
• Pandas & NumPy: For data manipulation and numerical operations.
• Scikit-learn & XGBoost: For building and training machine learning models.
• Uvicorn: An ASGI server for running the FastAPI application.

Frontend

• React: A JavaScript library for building user interfaces.
• Vite: A fast build tool and development server for modern web projects.
• Tailwind CSS: A utility-first CSS framework for rapid UI development.
• Recharts: A composable charting library built on React components.
• i18next: An internationalization framework for translating the application.

Setup and Installation

Prerequisites

• Python 3.10 or higher
• Node.js and npm

Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment:

   python3 -m venv .venv

3. Activate the virtual environment:

   • On macOS and Linux:

     source .venv/bin/activate

   • On Windows:

     .venv\\Scripts\\activate

4. Install the required Python packages:

   pip install -r requirements.txt

Frontend Setup

1. Navigate to the frontend directory:

   cd frontend

2. Install the required npm packages:

   npm install

Running the Application

Backend

1. Navigate to the backend directory and ensure your virtual environment is activated.

2. Run the data processing pipeline to generate predictions:

   python main.py

3. Start the FastAPI server:

   uvicorn app:app --host 0.0.0.0 --port 8000 --reload

Frontend

1. Navigate to the frontend directory.

2. Start the Vite development server:

   npm run dev

The application will be available at http://localhost:5173.