Abstract:
Stock market price prediction is a vital component for investors and financial analysts, offering the dual benefits of maximizing profits and managing risks effectively. This study delves into the application of sophisticated machine learning algorithms to forecast stock prices. It leverages a comprehensive dataset that spans several years and encompasses key financial metrics, including opening, closing prices, high, and low prices, as well as trading volumes. In this research, five regression models were employed to forecast future stock prices: Decision Tree Regressor, Linear Regression, AdaBoost Regressor, Gradient Boosting Regressor, and RandomForestRegressor. Training and testing samples were taken from the dataset in order to assess the performance of these models. The analysis shows that ensemble methods, specifically Gradient Boosting Regressor and RandomForestRegressor, deliver superior forecast accuracy than the other models. The study meticulously details the actions required in data preprocessing, the execution of the models, and a thorough performance evaluation. It sheds light on the strengths and weaknesses of each algorithm throughout the stock market forecasting. The results indicate that machine learning provides a solid foundation for creating predictive models that can accommodate the complex and dynamic nature of financial markets. This study highlights the opportunities for machine learning to enhance predictive analytics in finance, providing valuable insights for developing robust investment strategies.
