A Multivariable Regression Analysis of Air Quality using Statistical Approach
Abstract - There is a requirement to leverage air quality considerations among the people because of elevation within the pollution index these recent years. The most important contributory sources of pollution has become a misconception and is usually being forgotten. A survey was conducted across the European countries to record people’s perception on the pollution sources. Analysis shows the perpetual response from the respondents to being the trade and traffic. The sources of pollution per unit area are manifold and distinctive in nature. Exposures of pollution has corresponded to risk health factors and shows adverse result on health and development. Despite the high exposure of pollution, there is no caution taken to manage the sources.
Relative humidity, on the opposite hand is usually neglected and downplayed, however it seems to be a heavy drawback. It plays a major role in neutering our perception on air temperature and therefore the material that represent the air. Quantitative Air quality index that provides with correct measuring for ratio in terms of health and luxury, records the extent between 30-50% wetness within the air. Pollutants within the variety of chemical compounds area unit vulnerable to be gift within the air that cause venturesome risk factors. This study summarizes the main target of the influence of those chemical compounds on the air quality by the employment of sensing element devices planted on the fields of Italy. Utilizing the machine learning models within the field of regression, and a stress on feature extraction is drawn out on this study. Influence of chemical concentration and their result on the ratio is measured with the applied math metrics like RMSE, R^2 and Adjusted R^2 to make the ultimate outcome.
Keywords - Relative Humidity; Air Quality; Machine Learning; Feature Extraction; Feature Extraction; Regression; Prediction.