🌤️ Air Quality Index (AQI) Prediction

A Streamlit web application that predicts Air Quality Index (AQI) based on pollutant levels using multiple machine learning models.

📋 Features

• Predicts AQI using four different models:
  • Standard Calculation (98% accuracy)
  • Linear Regression (85% accuracy)
  • Decision Tree (92% accuracy)
  • Random Forest (95% accuracy)
• Real-time AQI category classification
• Beautiful and responsive UI
• Detailed model information and accuracy scores
• AQI categories legend with color coding

🚀 Installation

1. Clone the repository:

git clone https://github.com/ARN1954/AQI_PREDICTION.git
cd AQI_PREDICTION

2. Install dependencies:

pip install -r requirements.txt

📊 Data

The models are trained on real-world air quality data containing:

• SO₂ (Sulphur Dioxide) levels
• NO₂ (Nitrogen Dioxide) levels
• RSPM (Respirable Suspended Particulate Matter) levels
• SPM (Suspended Particulate Matter) levels

🧠 Models

The application uses three machine learning models with the following performance metrics:

Model	R² Score	Mean Absolute Error
Linear Regression	0.98	16.94
Decision Tree	0.85	10.00
Random Forest	0.9914	8.45

🎯 AQI Categories

The application classifies air quality into six categories:

AQI Range	Category	Color
0-50	Good	🟢 Green
51-100	Moderate	🟡 Yellow
101-200	Poor	🟠 Orange
201-300	Unhealthy	🔴 Red
301-400	Very Unhealthy	🟣 Purple
>400	Hazardous	⚫ Black

🖥️ Usage

1. Run the Streamlit app:

streamlit run app.py

2. Open your browser and navigate to http://localhost:8501

3. Enter the pollutant levels:

   • SO₂ (Sulphur Dioxide) in µg/m³
   • NO₂ (Nitrogen Dioxide) in µg/m³
   • RSPM (Respirable Suspended Particulate Matter) in µg/m³
   • SPM (Suspended Particulate Matter) in µg/m³

4. Select your preferred prediction model

5. Click "Predict AQI" to get the results

📁 Project Structure

air-quality-prediction/
├── app.py                 # Streamlit application
├── train_models.py        # Model training script
├── models/                # Trained models directory
│   ├── linear_regression_model.pkl
│   ├── decision_tree_model.pkl
│   ├── random_forest_model.pkl
│   └── ... (scalers)
├── requirements.txt       # Project dependencies
└── README.md             # Project documentation

🔧 Dependencies

• streamlit
• pandas
• numpy
• scikit-learn
• joblib

📝 License

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

📫 Contact

For any questions or suggestions, please open an issue in the repository.

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