The stock market is a complex system influenced by various factors, including economic indicators, geopolitical events, and investor sentiments. Traditional methods of stock market analysis often rely on statistical models and technical indicators, which may struggle to capture the intricate patterns and non-linear relationships present in financial data. This paper is about an innovative application which is designed to fill the gap between traditional stock market analysis and cutting-edge predictive modeling. The paper not only addresses the challenges associated with fragmented data and delayed analysis but also opens avenues for continuous monitoring and optimization of predictive models in response to dynamic market conditions. These models are seamlessly integrated into the application developed in the Analysis Phase, providing users with real-time predictions and valuable insights. Many machines learning (ML) and deep learning (DL) techniques have demonstrated to perform well in stock price prediction by prior research, and most people regard DL techniques them as one of the most accurate prediction methods, particularly when used for longer prediction ranges. In this research, after performing pre-processing steps like data normalization, we have employed an LSTM and GRU based models. Through training and testing, we determined the ideal settings for the optimizer, dropout, batch size, epochs, and other parameters. The outcome of comparing the LSTM network model with GRU we concluded that LSTM it is not suitable for short-term forecasting, and performs well for long-term forecasting whereas GRU performs well in both cases
