NajmiHassan1/Air_Pollution_Prediction_System

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

🌍 Air Pollution Prediction System

A machine learning-based web application that predicts PM2.5 levels and air quality using environmental and demographic factors.

📋 Overview

This system uses trained regression and classification models to predict:

• —PM2.5 Concentration (µg/m³) - Fine particulate matter levels
• —Air Quality Level - Classification into Good, Moderate, Poor, or Hazardous

🎯 Features

• —Real-time air quality predictions
• —User-friendly web interface built with Streamlit
• —Dual prediction capabilities (PM2.5 levels and quality classification)
• —Interactive input controls with validation
• —Color-coded results with health recommendations

📊 Dataset Information

Source: Air Quality and Pollution Assessment Dataset

• —Size: 5,000 samples
• —Features: 8 environmental and demographic variables
• —Target: Air quality levels (Good, Moderate, Poor, Hazardous)

Input Parameters:

• —Temperature (°C): Average regional temperature
• —Humidity (%): Relative humidity
• —PM10 (µg/m³): Coarse particulate matter
• —NO2 (ppb): Nitrogen dioxide levels
• —SO2 (ppb): Sulfur dioxide levels
• —CO (ppm): Carbon monoxide levels
• —Industrial Proximity (km): Distance to nearest industrial zone
• —Population Density (people/km²): Regional population density

🚀 Quick Start

Prerequisites

pip install streamlit numpy pandas pickle

File Structure

air-pollution-predictor/
├── app.py                      # Streamlit web interface
├── air_pollution_predictor.py  # Main prediction logic
├── regression_model.pkl        # Trained PM2.5 prediction model
├── classification_model.pkl    # Trained air quality classification model
└── README.md

🎮 How to Use

1. 1.Enter Environmental Data: Input temperature, humidity, and pollutant levels
2. 2.Set Location Factors: Specify industrial proximity and population density
3. 3.Get Predictions: Choose to predict PM2.5 levels, air quality, or both
4. 4.View Results: See color-coded predictions with health recommendations

🏥 Air Quality Standards

• —🟢 Good: Safe air quality with minimal health impact
• —🟡 Moderate: Acceptable for most people
• —🟠 Poor: May affect sensitive individuals
• —🔴 Hazardous: Health risk for everyone

🔧 Technical Details

• —Framework: Streamlit for web interface
• —Models: Scikit-learn based regression and classification
• —Input Validation: Built-in range checking and error handling
• —Caching: Optimized model loading for better performance

📁 Project Structure

• —air_pollution_predictor.py: Core prediction logic and model handling
• —app.py: Streamlit web application interface
• —Model files: Pre-trained pickle files for predictions

Made with ❤️ by Najmi Hassan