Predictive Modelling of Stock Market Prices Using Machine Learning Web App
Authors:-Akanksha Bhagwan Bangar, Dr. Santosh Jagtap
Abstract-The stock market is a dynamic environment influenced by numerous factors, making the prediction of stock prices a challenging yet critical task. Traditional methods often fall short due to the complex and volatile nature of financial markets. This project focuses on developing a machine learning-based web application for predicting stock prices, leveraging advanced algorithms to identify hidden patterns within historical data. The core of the application is built on the Long Short-Term Memory (LSTM) network, a specialized form of Recurrent Neural Network (RNN) designed for time series forecasting. LSTM networks excel in capturing long-term dependencies in sequential data, making them highly effective for financial predictions where past trends influence future movements. The model processes historical stock price data, analyzing trends, fluctuations, and patterns to predict future prices with a higher degree of accuracy. By maintaining an internal state, the LSTM can retain valuable information over time, providing robust forecasting capabilities. The web application offers an interactive interface where users can input stock symbols and view predicted price trends alongside real-time data. This feature enhances user engagement and decision-making processes, aiding investors in strategic planning. The project not only demonstrates the potential of machine learning in finance but also highlights the integration of predictive models into practical applications. The successful implementation of this system could contribute to more informed investment decisions, potentially yielding significant profits.
