🌍 Real-Time Air Quality Monitoring System using IoT and Machine Learning

This project predicts Nitrogen Dioxide (NO₂) levels in real-time using simulated IoT sensor data and live weather parameters from the OpenWeather API. It integrates data fusion, preprocessing, and ML-based predictions (XGBoost & LSTM) to alert users about air quality status via an interactive web dashboard with maps and charts.

🚀 Features

• 🔄 Real-time NO₂ prediction using live and simulated sensor data
• 🌤️ Weather data integration via OpenWeather API
• 🔀 Data fusion and preprocessing with feature scaling
• 🤖 ML-based prediction using XGBoost (tabular) and LSTM (time series)
• 🗺️ Interactive map visualization using Leaflet.js
• 📈 Dynamic multi-parameter charting using Chart.js
• ⚠️ Smart alerts based on NO₂ levels (Safe / Unsafe)
• 🖥️ Web dashboard interface built with Flask + HTML + JS

⚙️ How to Run

1. Clone the repo

   git clone https://github.com/deepikaeswaran/REAL-TIME-AIR-QUALITY-MONITORING-SYSTEM
   cd REAL-TIME-AIR-QUALITY-MONITORING-SYSTEM
   

2. Install requirements

   pip install -r requirements.txt

3. Train the model

   python train_model.py

4. Start the server

   python app.py

5. Access in browser

   http://127.0.0.1:5000

📊 Tech Stack

• Backend: Python, Flask
• Frontend: HTML, CSS, JavaScript
• Visualization: Leaflet.js, Chart.js
• ML Models: XGBoost, LSTM
• API: OpenWeather API

🧪 Output