Published Paper Details:

In today's economies, financial analysis is essential since it offers investors, business owners, and policymakers important information. The need for strong financial analysis has increased due to technology breakthroughs and the availability of abundant amounts of data, empowering stakeholders to make better decisions in the face of market uncertainty. In order to forecast stock prices and assess financial performance, this study focuses on applying machine learning techniques, including Linear Regression, Gradient Boosting, and Random Forest Regression. The results show how well these models work in navigating the intricacies of financial markets, as shown by the Root Mean Squared Error (RMSE) ratings. The paper also emphasizes the moral issues related to financial technology, arguing that algorithmic trading systems and prediction models should be used responsibly to maintain justice and transparency. This study opens the door for a data-driven strategy to investing in the future, promoting sustainable wealth creation and financial literacy while emphasizing morality and accountability.