• Damini Yadav
• Dr. Ashish Tamrakar
• Khusboo Sao
• MD. Arif Khan

This research explores by using a methodology that combines Principal Component Analysis (PCA) and Gradient Boosting Machine (GBM), the impact of climate change on biodiversity. Given the significant threat climate change poses to global biodiversity, understanding its effects is paramount for conservation efforts. GBM is renowned for its capacity to manage complex, nonlinear relationships, is employed to analyze extensive biodiversity datasets and pinpoint key climate variables influencing biodiversity patterns. Additionally, Through the use of PCA, the dimensionality underlying the data shrinks and hidden variables representing underlying patterns in biodiversity responses to climate change are revealed. By integrating these approaches, the objective of our study is to shed light on complex relationships between environmental degradation and biodiversity, offering insights into potential ecological shifts and guiding adaptive management strategies.

Climate Change, Biodiversity, Gradient Boosting Machine, GBM, Principal Component Analysis, PCA, Machine Learning, Conservation Biology

"PREDICTING BIODIVERSITY DYNAMICS: HARNESSING MACHINE LEARNING FOR CLIMATE CHANGE ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.p746-p753, April-2024, Available :http://www.jetir.org/papers/JETIR2404G98.pdf

"PREDICTING BIODIVERSITY DYNAMICS: HARNESSING MACHINE LEARNING FOR CLIMATE CHANGE ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppp746-p753, April-2024, Available at : http://www.jetir.org/papers/JETIR2404G98.pdf