  International trade policies have recently garnered attention for limiting
cross-border exchange of essential goods (e.g. steel, aluminum, soybeans, and
beef). Since trade critically affects employment and wages, predicting future
patterns of trade is a high-priority for policy makers around the world. While
traditional economic models aim to be reliable predictors, we consider the
possibility that Machine Learning (ML) techniques allow for better predictions
to inform policy decisions. Open-government data provide the fuel to power the
algorithms that can explain and forecast trade flows to inform policies. Data
collected in this article describe international trade transactions and
commonly associated economic factors. Machine learning (ML) models deployed
include: ARIMA, GBoosting, XGBoosting, and LightGBM for predicting future trade
patterns, and K-Means clustering of countries according to economic factors.
Unlike short-term and subjective (straight-line) projections and medium-term
(aggre-gated) projections, ML methods provide a range of data-driven and
interpretable projections for individual commodities. Models, their results,
and policies are introduced and evaluated for prediction quality.

Econometrics;Machine Learning