Air Quality Prediction using Seaborn and TensorFlow

 Air quality is considered as a vital issue in the current world and is the underlying driver of sicknesses identified with respiratory organ, skin malignant growth, corrosive downpour and a worldwide temperature alteration. Anticipating air quality has been the consistent test with the developing industrialization, vehicles out and about, deforestation and different variables. Air contamination has been the issue of the entire world. In this paper, we propose to foresee the air nature of a specific spot, with the information gathered in past and take preventive measure to stop the disaster. We will utilize Spearmans Correlation as information used to foresee air quality is non straight and monotonic. Spearmans Correlation coefficient rs can invigorate us of the connection between highlights of information. 

by Rahul Kumar Sharma | Kuldeep Baban Vayadande | Rahul Ranjan "Air Quality Prediction using Seaborn and TensorFlow" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, 

URL: https://www.ijtsrd.com/papers/ijtsrd37975.pdf

Paper URL : https://www.ijtsrd.com/computer-science/other/37975/air-quality-prediction-using-seaborn-and-tensorflow/rahul-kumar-sharma

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