Air Quality Prediction System using Python ML

Posted 2025-08-21 16:57:44

“Air Quality Prediction System” is a web-based application that integrates machine learning with the Django framework. This project is designed to measure, track and analyze the concentration of various pollutants in the atmosphere to determine the quality of air in a specific location.

This system uses sensors or real-time data from air quality monitoring stations to detect harmful pollutants such as PM2.5, PM10, Carbon Monoxide (CO), Sulphur Dioxide (SO₂), Nitrogen Dioxide (NO₂), and Ozone (O₃). The collected data is processed, stored, and displayed in an understandable format, enabling individuals, researchers, and government authorities to make informed decisions.

The Air Quality Monitoring System also provides Air Quality Index (AQI) values, categorizing air conditions into levels such as Good, Moderate, Poor, Very Poor, and Severe. These levels help people take necessary precautions, such as wearing masks or avoiding outdoor activities during poor air quality days.

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Tech Stack Used

Frontend / Web Interface:

Django (Python Web Framework) — Used to create the web interface for user input, displaying predictions, and managing data
HTML5, CSS3, JavaScript — For rendering and styling web pages
Bootstrap (optional) — For responsive UI components
Django Templates — For dynamic web page rendering

Machine Learning / Backend Logic:

scikit-learn — Machine Learning library used to implement algorithms like Logistic Regression, Decision Tree, Random Forest, KNN
NumPy — For numerical operations and matrix manipulation
Pandas — For handling and preprocessing datasets
joblib — To save and load the trained machine learning model

Database:

SQLite — Lightweight relational database used to store user data and predictions
Django ORM (Object Relational Mapper) — Handles interaction between Django models and the SQLite database

Tools & Environment:

Python 3.x — Core programming language used
PyCharm — IDE for development
Virtualenv / pip — For managing dependencies

Key Features of the Proposed System

IoT Sensor Network
Multiple air quality monitoring stations equipped with sensors to measure major pollutants such as PM2.5, PM10, CO, NO₂, SO₂, and O₃.
Real-time transmission of data to a central server via the internet.
Centralized Data Management
A robust database to store and manage large volumes of real-time and historical AQI data.
Station-wise and date-wise filtering for quick retrieval and analysis.
Real-time AQI Calculation
Automatic computation of AQI based on pollutant concentration using national or international AQI standards.
Categorization into levels such as Good, Moderate, Unhealthy, Very Unhealthy, and Hazardous.
Predictive Analysis
Implementation of machine learning models to forecast future AQI levels based on historical trends and environmental factors.

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