Abstract
This paper leverages the LightGBM Ensemble Method to predict stock prices. First, the time features are from the dates and these generated features are used to build a regression model. Experiments are performed on the Tesla and the Coca Cola stock historical data to show the effectiveness of the method in predicting stock prices
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