Major Project · Web Development

Air Quality Project

This project is a web-based Air Quality Detection and Prediction System developed using Django and Python. It helps users estimate air quality by entering pollutant values through an online form. The system processes the values, …

Difficulty Level

Intermediate

Core Modules

4 Modules

Project Overview

The Air Quality Prediction System is a Django-based web application designed to predict the air quality category from pollutant concentration values entered by the user. The system accepts values for major air pollutants such as PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toluene, and Xylene. Based on these inputs, it predicts the Air Quality Index (AQI) bucket such as Good, Satisfactory, Moderate, Poor, Very Poor, or Severe.

The application also provides user registration and login functionality, allowing users to access the prediction page after authentication. The prediction logic supports lookup from the `city_day.csv` dataset when available and also includes AQI breakpoint-based calculation as a fallback. The system presents the predicted AQI result along with a pollutant distribution chart, making the output easier to understand visually.

Module Breakdown 4 Modules

User Registration

The system provides a registration page where new users can create an account. The registration form collects user details such as first name, last name, email, username, password, and password confirmation. Django's built-in `UserCreationForm` is used for secure user registration.

User Login

Registered users can log in using their username and password. The system authenticates the credentials using Django's authentication framework. After successful login, the user is redirected to the air quality prediction page.

Air Quality Input Form

The prediction page contains an input form where users enter pollutant concentration values. The form accepts values for PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toluene, and Xylene. Django forms are used to validate numeric input before prediction.

AQI Prediction

The system predicts the AQI bucket using the entered pollutant values. If `city_day.csv` is available, the system attempts to match the entered pollutant values with dataset records and returns the corresponding AQI bucket. If the dataset is missing or no exact match is found, the system calculates AQI using pollutant breakpoint ranges.

Technology Stack

Python Data Science & Analytics HTML CSS3 Django SQLite

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Project Abstract

The Air Quality Prediction System is a Django-based web application designed to predict the air quality category from pollutant concentration values entered by the user. The system accepts values for major air pollutants such as PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toluene, and Xylene. Based on these inputs, it predicts the Air Quality Index (AQI) bucket such as Good, Satisfactory, Moderate, Poor, Very Poor, or Severe. The application also provides user registration and login functionality, allowing users to access the prediction page after authentication. The prediction logic supports lookup from the `city_day.csv` dataset when available and also includes AQI breakpoint-based calculation as a fallback. The system presents the predicted AQI result along with a pollutant distribution chart, making the output easier to understand visually.

Project Kit Includes

Complete Source Code

Database Schema & Setup Guide

Project Report Template

Completion Certificate

Installation & Deployment Guide

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The Air Quality Prediction System is a Django-based web application designed to predict the air quality category from pollutant concentration values entered by the user. The system accepts values for major air pollutants such as PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toluene, and Xylene. Based on these inputs, it predicts the Air Quality Index (AQI) bucket such as Good, Satisfactory, Moderate, Poor, Very Poor, or Severe. The application also provides user registration and login functionality, allowing users to access the prediction page after authentication. The prediction logic supports lookup from the `city_day.csv` dataset when available and also includes AQI breakpoint-based calculation as a fallback. The system presents the predicted AQI result along with a pollutant distribution chart, making the output easier to understand visually.