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Air pollution is one of the major environmental
issues that affect the health and well-being of people living in
cities around the world. For this purpose, it is important that the
Air Quality Index (AQI) is predicted with maximum accuracy.
This research aims to propose a system for the prediction of the
Air Quality Index using machine learning algorithms. Various
algorithms are used for the prediction of the Air Quality Index.
These include the use of the Random Forest algorithm, Support
Vector Machine (SVM), Decision Tree, and Linear Regression.
The system has been implemented using Python and a web
application based on the Django framework. The experimental
results show that the use of machine learning algorithms for the
prediction of the Air Quality Index is quite effective.
Keywords:
Air Quality Index (AQI), Machine Learning, Air Pollution Prediction, Environmental Monitoring, Random Forest, Support Vector Machine
Cite Article:
"Improvement in Air Quality Index (AQI) using Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b227-b229, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603127.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator