STOCKDIARY: Post-Development Analysis and Performance Evaluation of an Advanced Stock Market Prediction Platform

In the rapidly changing financial landscape, accurate stock market prediction is essential for investors to optimize their portfolios and manage risks effectively. STOCKDIARY is an innovative platform that leverages state-of-the-art technologies like Python, Streamlit, and Jupyter to provide real-time insights and predictions to its users. This paper conducts a thorough post-deployment evaluation of STOCKDIARY focusing processes, user engagement, predictive accuracy, and overall performance. Utilizing sophisticated predictive models trained on extensive historical data, including Linear Regression, Support Vector Regression, and Random Forest, STOCKDIARY delivers timely and precise predictions. Its user-friendly interface, developed with Streamlit, offers intuitive visualization tools, empowering users to analyze market trends effectively. Evaluation metrics such as Mean Squared Error and R-squared values demonstrate the platform’s reliability. Continuous user feedback informs ongoing enhancements, including the integration of advanced analytics and further refinement of the user experience. STOCKDIARY emerges as a valuable tool for investors, providing actionable insights for confident decision-making in today’s complex financial markets.