Air Quality Analysis And Prediction For Indian Cities And States

Abstract

1. Introduction

This project aims to analyze and predict the air quality index of various cities and states in India using machine learning. The dataset used in this project contains historical data of air quality index readings from various monitoring stations across India.

in my city indore there are several places that show aqi and chemical levels troughout the day in a tabular format, i researched about this topic and found out this research paper https://www.diva-portal.org/smash/get/diva2:1681590/FULLTEXT02 in this research paper they have just taken a small amount of data and shoved it into models

2. Objective

This project aims to analyze and predict the air quality index of various cities and states in India using machine learning. The dataset used in this project contains historical data of air quality index readings from various monitoring stations across India.

This project demonstrates how Jupyter Notebook can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/aryanrathore1012/Air_Quality_Analysis_and_Prediction_for_Indian_Cities_and_States.git
cd Air_Quality_Analysis_and_Prediction_for_Indian_Cities_and_States

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by aryanrathore1012 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.