Leveraging machine learning algorithms to predict stock trends based on company data.

Due to the overall continual flow of news, announcements, worldwide data points, and so on, stocks are volatile and unpredictable. This is affected by market volatility and a variety of other variables in the research, both independent and dependent, that impact the stocks' market˘ value. These variables make it difficult for a stock market expert to precisely predicts the market's peaks and troughs. The major purpose of this essay is to anticipate market stock stability in the future. The studyafocuses˘ on the use of two approaches, gradient boosted decision trees (using XG Boost) and random forests, to forecast whether stock values will rise or fall over the following n days. [ABSTRACT FROM AUTHOR]