  In the dynamic landscape of continuous change, Machine Learning (ML)
"nowcasting" models offer a distinct advantage for informed decision-making in
both public and private sectors. This study introduces ML-based GDP growth
projection models for monthly rates in Peru, integrating structured
macroeconomic indicators with high-frequency unstructured sentiment variables.
Analyzing data from January 2007 to May 2023, encompassing 91 leading economic
indicators, the study evaluates six ML algorithms to identify optimal
predictors. Findings highlight the superior predictive capability of ML models
using unstructured data, particularly Gradient Boosting Machine, LASSO, and
Elastic Net, exhibiting a 20% to 25% reduction in prediction errors compared to
traditional AR and Dynamic Factor Models (DFM). This enhanced performance is
attributed to better handling of data of ML models in high-uncertainty periods,
such as economic crises.
