Exploring Biodiversity Through Taxonomic Data: A Comprehensive Analysis

Abstract

This Project has analysis of  a dataset of survey papers to uncover trends and insights into research developments across various fields. By exploring attributes such as taxonomy, release date, and summaries, we visualize paper trends over time. Additionally, we classify the papers using machine learning techniques, addressing class imbalance through data augmentation and stratified sampling. Implementing stratified cross-validation ensures robust model evaluation. Ultimately, this work aims to enhance understanding of survey literature and provide valuable insights for researchers and practitioners in their respective domains.