Price Prediction for Starbucks Corporation Based on Random Forest, Linear Regression, and Decision Tree

The outbreak of the virus COVID-19 in 2019 has dealt an unprecedented blow to global economy. Contemporarily, economy is recovery stage after end epidemic, and economic recession likely be future trend terms current status. In this paper, three state-of-art multivariate supervised learning regression scenarios (i.e., Random Forest, Linear Regression, Decision Tree) are implemented predict stock price Starbucks. Through comparison, it concluded that Regression Forest most accurate models expected used future. Moreover, investing Starbucks not a wise choice at time. It hoped reduce investor losses by choosing optimal model for predictions minimizing recession. These results shed light on guiding further exploration targeted analysis new environment.