Machine Learning-Based Comparative Analysis of COVID-19 Infected Cases with GDP and World Happiness Report

Meenu Gupta; Rakesh Kumar; Shanon Sharma; Mitashi Bansal; Ahmed J. Obaid

doi:10.1007/978-981-19-9512-5_32

Machine Learning-Based Comparative Analysis of COVID-19 Infected Cases with GDP and World Happiness Report

Meenu Gupta
, Rakesh Kumar
, Shanon Sharma
, Mitashi Bansal
, Ahmed J. Obaid

Chandigarh University
Amity University, Noida
University of Kufa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Today, the world has been struck by an indomitable virus and an unprecedented situation arising due to COVID-19 sickness. The current circumstance is an extremely extraordinary circumstance, and each country in this world is attempting to relieve and reduce its spread. In this paper, we will research on how to pre-process and combine datasets to ascertain required measures and set them up for an investigation. This paper aims at reviewing the correlation between the above-mentioned factors. COVID-19 dataset was imported and set up for examination by dropping segments and collecting columns. After deciding on and computing a decent measure, the examination results were visualized using seaborn. The factors like gross domestic product (GDP), happiness, and social support are the factors that played a decisive role in the impact of the coronavirus on a nation.

Original language	English
Title of host publication	Micro-Electronics and Telecommunication Engineering - Proceedings of 6th ICMETE 2022
Editors	Devendra Kumar Sharma, Rohit Sharma, Sheng-Lung Peng, Gwanggil Jeon
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	345-355
Number of pages	11
ISBN (Print)	9789811995118
DOIs	https://doi.org/10.1007/978-981-19-9512-5_32
State	Published - 2023
Externally published	Yes
Event	6th International Conference on Microelectronics and Telecommunication Engineering, ICMETE 2022 - Ghaziabad, India Duration: 1 Jun 2023 → 2 Jun 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	617 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	6th International Conference on Microelectronics and Telecommunication Engineering, ICMETE 2022
Country/Territory	India
City	Ghaziabad
Period	1/06/23 → 2/06/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

COVID-19 world happiness report
Correlation
GDP per capita
Life expectancy
Pearson coefficient

Access to Document

10.1007/978-981-19-9512-5_32

Cite this

Gupta, M., Kumar, R., Sharma, S., Bansal, M., & Obaid, A. J. (2023). Machine Learning-Based Comparative Analysis of COVID-19 Infected Cases with GDP and World Happiness Report. In D. K. Sharma, R. Sharma, S.-L. Peng, & G. Jeon (Eds.), Micro-Electronics and Telecommunication Engineering - Proceedings of 6th ICMETE 2022 (pp. 345-355). (Lecture Notes in Networks and Systems; Vol. 617 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-9512-5_32

Gupta, Meenu ; Kumar, Rakesh ; Sharma, Shanon et al. / Machine Learning-Based Comparative Analysis of COVID-19 Infected Cases with GDP and World Happiness Report. Micro-Electronics and Telecommunication Engineering - Proceedings of 6th ICMETE 2022. editor / Devendra Kumar Sharma ; Rohit Sharma ; Sheng-Lung Peng ; Gwanggil Jeon. Springer Science and Business Media Deutschland GmbH, 2023. pp. 345-355 (Lecture Notes in Networks and Systems).

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abstract = "Today, the world has been struck by an indomitable virus and an unprecedented situation arising due to COVID-19 sickness. The current circumstance is an extremely extraordinary circumstance, and each country in this world is attempting to relieve and reduce its spread. In this paper, we will research on how to pre-process and combine datasets to ascertain required measures and set them up for an investigation. This paper aims at reviewing the correlation between the above-mentioned factors. COVID-19 dataset was imported and set up for examination by dropping segments and collecting columns. After deciding on and computing a decent measure, the examination results were visualized using seaborn. The factors like gross domestic product (GDP), happiness, and social support are the factors that played a decisive role in the impact of the coronavirus on a nation.",

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Gupta, M, Kumar, R, Sharma, S, Bansal, M & Obaid, AJ 2023, Machine Learning-Based Comparative Analysis of COVID-19 Infected Cases with GDP and World Happiness Report. in DK Sharma, R Sharma, S-L Peng & G Jeon (eds), Micro-Electronics and Telecommunication Engineering - Proceedings of 6th ICMETE 2022. Lecture Notes in Networks and Systems, vol. 617 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 345-355, 6th International Conference on Microelectronics and Telecommunication Engineering, ICMETE 2022, Ghaziabad, India, 1/06/23. https://doi.org/10.1007/978-981-19-9512-5_32

Machine Learning-Based Comparative Analysis of COVID-19 Infected Cases with GDP and World Happiness Report. / Gupta, Meenu; Kumar, Rakesh; Sharma, Shanon et al.
Micro-Electronics and Telecommunication Engineering - Proceedings of 6th ICMETE 2022. ed. / Devendra Kumar Sharma; Rohit Sharma; Sheng-Lung Peng; Gwanggil Jeon. Springer Science and Business Media Deutschland GmbH, 2023. p. 345-355 (Lecture Notes in Networks and Systems; Vol. 617 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Gupta M, Kumar R, Sharma S, Bansal M, Obaid AJ. Machine Learning-Based Comparative Analysis of COVID-19 Infected Cases with GDP and World Happiness Report. In Sharma DK, Sharma R, Peng SL, Jeon G, editors, Micro-Electronics and Telecommunication Engineering - Proceedings of 6th ICMETE 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 345-355. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-19-9512-5_32

Machine Learning-Based Comparative Analysis of COVID-19 Infected Cases with GDP and World Happiness Report

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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Cite this