A MACHINE LEARNING LINEAR REGRESSION MODEL TO PREDICT FUTURE GIANT PANDA POPULATION

Received: 29th September 2021; Revised: 3rd November 2021, 9th November 2021, 11th November 2021; Accepted: 14th November 2021

Authors

  • Kumud Dave Student (B.Sc.), Guru Nanak Girls College, Udaipur, Rajasthan, India
  • Satyendra Barber Assistant Professor, Guru Nanak Girls College, Udaipur, Rajasthan, India
  • Pooja Verma Assistant Professor, Guru Nanak Girls College, Udaipur, Rajasthan, India

DOI:

https://doi.org/10.20319/lijhls.2021.7.123130

Keywords:

Conservation, Giant Panda, Machine Learning, Linear Regression, Deforestation

Abstract

Increasingly used as the insignia of China, the zaftig and enchanting Giant Panda lives on mountains of southwest China. The Giant Panda is on the WWF logo and is known as “National Treasure” in China. In this study, we predict the future Giant Panda population by using machine learning algorithms of the simple linear regression model. We take different variables to predict the next 30 years of the Giant Panda population. Focusing on the factors which affect the Giant Panda population. We take several parameters for this research like Bamboo Population, Annual Rainfall in China, Carbon Stock in Bamboo Stems, Deforestation, and Human Influence and Population of Giant Panda. Despite their peak status and relative deficiency of natural predators, pandas are still at risk and multiple intimidations from human influence have left just over 1,800 Pandas in the forest. To be ready for future troubles it is mandatory to have a pre-look of some conditions so that we can be prepared for that. Substantially, Endangered species at the edge of extinction are kept in extra special conservation. The machine learning algorithms developed with a wide-ranging of training datasets that help to find results faster and accurately.

References

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Published

2021-11-17

How to Cite

Dave, K., Barber, S., & Verma, P. (2021). A MACHINE LEARNING LINEAR REGRESSION MODEL TO PREDICT FUTURE GIANT PANDA POPULATION: Received: 29th September 2021; Revised: 3rd November 2021, 9th November 2021, 11th November 2021; Accepted: 14th November 2021. LIFE: International Journal of Health and Life-Sciences, 7, 123–130. https://doi.org/10.20319/lijhls.2021.7.123130

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Articles