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ABSTRACT
We provided machine learning application in this paper. Predicting air quality is necessary step to be taken by government as it is becoming the major concern among the health of human beings. Air quality Index measure the quality of air. Various air pollutants causing air pollution are Carbon dioxide, Nitrogen dioxide, carbon monoxide etc that are released from burning of natural gas, coal and wood, industries, vehicles etc. Air Pollution can cause severe disease like lungs cancer, brain disease and even lead to death. Machine learning algorithms helps in determining the air quality index. Various research is being done in this field but still results are still not accurate. Dataset are available from Kaggle, air quality monitoring sites and divided into two Training and Testing. Machine Learning algorithms employed for this are Linear Regression, Decision Tree, Random Forest, Artificial Neural Network, Support Vector Machine.
INTRODUCTION
Air pollution is dangerous for human health and should be decrease fast in urban and rural areas so it is necessary to predict the quality of air accurately. There are many types of pollution like water pollution, air pollution, soil pollution etc but most important among these is air pollution which should be controlled immediately as humans inhale oxygen through air. There are various causes of air pollution. Outdoor air pollution caused by industries, factories, vehicles and Indoor air pollution is caused if air inside the house is contaminated by smokes, chemicals, smell. Two types of Pollutants that is causing air pollution are Primary Pollutants and Secondary Pollutants.
CONCLUSION
Lots of research had been done and different algorithms comes out to be superior with different conditions like pollutant chosen and area selection. Various algorithms had taken meteorological data like temperature, wind speed, humidity in predicting accurately the upcoming pollutant level. Neural Network and boosting model comes out to be superior than other algorithms.
