MACHINE LEARNING METHODS FOR UNDERSTANDING BIODIVERSITY AND ITS CONSERVATION

The exponential expansion of digital data and advances in machine learning techniques present great opportunities for biodiversity research. This paper aims to explore the application of machine learning algorithms to analyze and interpret some biodiversity data pertaining to Class Insecta, specifically focusing on Order Odonata. This study seeks to demonstrate the power of machine learning in generating valuable insights that facilitate species identification and contribute to the advancement of conservation efforts. This paper encompasses the application of various machine learning algorithms to data from Class Insecta, Order Odonata, for classification, clustering and prediction tasks, and the evaluation of their performance.