Authors :
Mohammad Ishaque Ali, K. Yasudha
Volume/Issue :
Volume 5 - 2020, Issue 4 - April
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/2KMjjun
Abstract :
One of the major issues we are facing all
around the world is “GLOBAL WARMING” and its
effects on our daily life which is only due to poor Air
quality. It is something when the earth’s surface
temperature increases slowly and makes the air quality
poor. The poor air quality affects human health in
many ways. The Machine Learning Algorithm (ML), is
used to predict Air quality Index. The main focus of the
paper is to establish the relationship between dependent
data and independent data. Here, we go through the
various feature engineering processes to analyze that
which machine learning algorithm must be used that
can give the best result and less overfitting.
Keywords :
Air Pollution, Particulate matter, Decision Tree Regression, Feature Engineering, Human Freedom Index, Linear Regression, Machine Learning, Mutual Information, Random Forest Regression.
One of the major issues we are facing all
around the world is “GLOBAL WARMING” and its
effects on our daily life which is only due to poor Air
quality. It is something when the earth’s surface
temperature increases slowly and makes the air quality
poor. The poor air quality affects human health in
many ways. The Machine Learning Algorithm (ML), is
used to predict Air quality Index. The main focus of the
paper is to establish the relationship between dependent
data and independent data. Here, we go through the
various feature engineering processes to analyze that
which machine learning algorithm must be used that
can give the best result and less overfitting.
Keywords :
Air Pollution, Particulate matter, Decision Tree Regression, Feature Engineering, Human Freedom Index, Linear Regression, Machine Learning, Mutual Information, Random Forest Regression.
