Air Quality Index Prediction with the Implementation of Linear Regression - A Technical Paper

Authors

  • Chakraborty S Dept. of Computer Science, University of Engineering and Management, Kolkata, India
  • Guha K Dept. of Computer Science, University of Engineering and Management, Kolkata, India

DOI:

https://doi.org/10.26438/ijcse/v8i8.3948

Keywords:

Air Quality Index, Linear Regression, Scikits Learn, Seaborn plot,, Heat Map, Mean Absolute Error (MAE),, Mean Squared Error(MSE), Root Mean Squared Error (RMSE), Pickle

Abstract

Within the last few years, an intense curiosity has been progressed by the people in the daily air quality circumstances to which they are encountered. Directed by the growing consciousness of the physical state of air pollution exposure, especially by most sensitive sub–populations such as children and the elderly, short–term air pollution forecasts are being accentuated progressively by local authorities. The Air Quality Index (AQI) is the value implemented to estimate the quality of the air at a certain location. The components are estimated with the implementation of the covariance of the input data matrix.

References

[1] https://seaborn.pydata.org/generated/seaborn.heatmap.html. Heat Map correlation and plotting

[2] https://seaborn.pydata.org/introduction.html. Implementation of Seaborn Library module

[3] https://en.tutiempo.net/climate/india.html. Dataset has been downloaded from this website and implemented for Research

[4]https://www.datacamp.com/community/tutorials/pickle-python-tutorial?utm_source=adwords_ppc&utm_campaignid=10267161064&utm_adgroupid=102842301792&utm_device=c&utm_keyword=&utm_matchtype=b&utm_network=g&utm_adpostion=&utm_creative=332602034358&utm_targetid=aud-299261629574:dsa-429603003980&utm_loc_interest_ms=&utm_loc_physical_ms=9061792&gclid=Cj0KCQjwo6D4BRDgARIsAA6uN1_TSGS3sO7w32Itj0gHy4FyQpjbTL54P0Qz-0rZ2y63NZnpT_PLFjIaAueoEALw_wcB. Imported Pickle module.

[5]https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html#:~:text=An%20extra-trees%20regressor.,accuracy%20and%20control%20over-fitting.&text=If%20int%2C%20then%20consider%20min_samples_split%20as%20the%20minimum%20number. Extra Regressor Classifier

[6] https://machinelearningmastery.com/feature-selection-machine-learning-python/Feature Selection

[7] https://towardsdatascience.com/interpreting-the-coefficients-of-linear-regression-cc31d4c6f235. Interpreting coefficients

[8] https://scikit-learn.org/stable/modules/model_evaluation.html. Regression evaluation Metric

[9] Github link:- https://github.com/Soumyajit567/Air-Quality-Index. This is my GitHub project link. The code is done in Jupyter Notebook and uploaded to GitHub.

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Published

2020-08-31
CITATION
DOI: 10.26438/ijcse/v8i8.3948
Published: 2020-08-31

How to Cite

[1]
S. Chakraborty and K. Guha, “Air Quality Index Prediction with the Implementation of Linear Regression - A Technical Paper”, Int. J. Comp. Sci. Eng., vol. 8, no. 8, pp. 39–48, Aug. 2020.

Issue

Vol. 8 No. 8 (2020): IJCSE August Edition

Section

Research Article

License

Creative Commons License

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