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What is Predicting Trade Agreement Success With Machine Learning about?

This document discusses the use of machine learning, particularly neural networks with PyTorch, to predict the success of trade agreements based on factors like deal size, political stability, and negotiation strategy. It outlines the model-building process, including data preparation, architecture, and evaluation, achieving 80-90% accuracy. The predictive model aims to assist trade negotiators in strategic planning, risk assessment, and resource allocation, with potential for future enhancements.

Author

fink.emili

Language

EN