HudsonLubinga/EAfrica_CITES_Wildlife_Trade_Database_ML_Classification_Model

We collected a comprehensive and accurate dataset of taxonomic features and other characteristics of wildlife species obtained from East African countries between 2018 and 2021 from CITES Wildlife Trade Database (kaggle.com) # Machine Learning Classification Model for Identifying Wildlife Species in East Africa # By Hudson Nandere Lubinga / Francis Lowu Xavier ### Dataset We collected a comprehensive and accurate dataset of taxonomic features and other characteristics of wildlife species obtained from East African countries between 2018 and 2021 from CITES Wildlife Trade Database (kaggle.com) ## General OVerview: The decline in global biodiversity is a pressing concern due to human activities, leading to millions of species at risk of extinction. East Africa is especially affected by habitat destruction, poaching, and climate change, resulting in significant losses in wildlife populations. Machine learning (ML) has demonstrated potential in identifying species, especially in camera trap images, acoustic recordings, and genetic data. However, there is a need to further explore the use of ML in identifying wildlife species in East Africa. To address this need, we developed ML classification models to identify wildlife species in East Africa. Our dataset included taxonomic features and characteristics of wildlife species from East African countries between 2018 and 2021. We used the random forest algorithm, which is suitable for complex datasets with multiple features. Our evaluation achieved an accuracy of 63.4% and a baseline score of 8.02%, showing the potential of our models in identifying wildlife species in East Africa. Our study could contribute to wildlife conservation by detecting and preventing illegal wildlife trade activities, monitoring population trends, assessing the impact of human activities on different species in East Africa, and preserving biodiversity. ## Discussion of Findings: The machine learning-based classification models developed in this study aim to identify wildlife species in East Africa with implications for conservation and management. The dataset used for this study was collected from CITES Wildl …