Open AccessAnalysis & Demonstration of Impact of Air Pollution
Author(s) -
D. Saha,
Shashikant Patil
Publication year - 2020
Publication title -
international journal of modern trends in science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-3778
DOI - 10.46501/ijmtst060619
Subject(s) - multicollinearity , regression analysis , statistics , logistic regression , linear regression , econometrics , stepwise regression , air pollution , variables , air quality index , variance inflation factor , regression , variable (mathematics) , mathematics , meteorology , geography , mathematical analysis , chemistry , organic chemistry
In this study we have analyzed the impact of air pollution in day to day life in all aspects. The main focusof this contribution is learning about modeling of data by supervised algorithms i.e. (Linear Regression(regression) and Logistic Regression (classification) and its consequences. This particular analysis ofAirPollution Impact (India & US), and factors that affects AQI. The dataset we have used comprisesconcentration of pollutants and there is needof each of it for calculating the air quality index, so that is beencalculated further in the process and has been utilized in analysis. Here we also seen the combination of theindependent variables (Interaction effect) and its impact on dependent variable and the accuracy of the modelvariation as well as interdependence/ correlation (Multicollinearity) between various independent variableand its adverse effect on the dependent variable and on the given data model. The solution to the problems ofmulticollinearity is also been discussed in the following kernel i.e. Regularization and Stepwise Regression.