Abstract: The stock market is a dynamic and complex system influenced by various factors such as financial indicators, market sentiment, political events, and economic conditions. Predicting stock prices is a challenging task due to the non-linear and volatile nature of the market. This project aims to analyze historical stock market data and predict future prices using deep learning techniques, particularly Long Short-Term Memory (LSTM) networks. By integrating historical price trends and sentiment analysis of financial news, we enhance the accuracy of predictions. The dataset includes 20 years of stock prices and recent sentiment scores from news headlines. The LSTM model is trained on this combined dataset to learn temporal patterns and market behavior. A user-friendly web interface developed using Flask allows users to input a stock ticker and receive the next day’s predicted price. This project demonstrates the potential of AI in financial forecasting and provides a tool for investors to make data-driven decisions.

| DOI: 10.17148/IJIREEICE.2025.13511