A Study on Reducing Illegal Wildlife Trade Based on Data-Driven Predictive Model

As an important part of the ecological environment and the main part of biodiversity, wildlife is a very important resource for the development of human civilization, and has increasingly become a hotspot and a focus of attention around the world. The aim of this paper is to provide global cooperation and comprehensive strategies for the systematic study of the current illegal wildlife trade, which in turn protects the valuable wildlife resources on earth. In this paper, the keyword “illegal wildlife” is used as the research term, and the relevant data collected is visualized and processed. A correlation analysis model is established to confirm the indicators, and a hierarchical analysis method is used to filter the target customers. Literature is cited to justify the selection of customers. Data-driven analysis based on linear regression modeling is used to convince the client to implement our project by predicting the illegal wildlife trade data for the next few years after the project is landed. Multiple linear regression and time series analysis models are then developed to explore the measurable impact of project implementation on illegal wildlife trade. Based on the sensitivity analysis, the likelihood that the project will be implemented with the desired objectives is examined as well.