  Understanding the business cycle is crucial for building economic stability,
guiding business planning, and informing investment decisions. The business
cycle refers to the recurring pattern of expansion and contraction in economic
activity over time. Economic analysis is inherently complex, incorporating a
myriad of factors (such as macroeconomic indicators, political decisions). This
complexity makes it challenging to fully account for all variables when
determining the current state of the economy and predicting its future
trajectory in the upcoming months. The objective of this study is to
investigate the capacity of machine learning models in automatically analyzing
the state of the economic, with the goal of forecasting business phases
(expansion, slowdown, recession and recovery) in the United States and the
EuroZone. We compared three different machine learning approaches to classify
the phases of the business cycle, and among them, the Multinomial Logistic
Regression (MLR) achieved the best results. Specifically, MLR got the best
results by achieving the accuracy of 65.25% (Top1) and 84.74% (Top2) for the
EuroZone and 75% (Top1) and 92.14% (Top2) for the United States. These results
demonstrate the potential of machine learning techniques to predict business
cycles accurately, which can aid in making informed decisions in the fields of
economics and finance.
