Stock Sage Navigator: Guiding Investors Through Market Complexities

In this modern era where making right investment decisions is very tough for single investors. Stock Price Analysis uses statistical data modeling and now in the emerging technologies, artificial intelligence in the depicting the future price of a specific stock. Even though new methods like neural network gave a push to the accuracy, the market changes are still a hurdle. Constant work is being carried out to increase robustness to try to include data of other types to represent market conditions quite accurately. Of-course there is a huge advantage for retail traders since there is a large information flow, fluctuations in price and professional approaches of large investors. This paper therefore aims to focus on implementing an advanced web application for stock analysis and prediction to meet this need. Developed with Python, the app incorporates a machine learning-based stock price prediction, sentiment analysis of the financial media, technical indicators, and multi-stock analysis. Putting the mentioned aspects together, the tool is designed to help retail investors to gain access to market insight that was previously feasible only for the professional traders. Putting the mentioned aspects together, the tool is designed to help retail investors to gain access to market insight that was previously feasible only for the professional traders. The focus of the research is on the synergy of machine learning, natural language processing, and technical analysis to improve the stock market analysis and help people who invest in the stock market to better understand it.