Stock Market Prediction Using Deep Learning and Stream lit

The stock market is characterized by high volatility, non-linearity, and dynamic fluctuations, making accurate prediction a challenging research problem. Traditional statistical approaches often fail to capture the temporal dependencies and complex patterns in financial data. With the advancement of artificial intelligence, particularly deep learning, the feasibility of developing reliable forecasting models has significantly increased. This paper presents a predictive framework for stock market trend analysis using Long Short-Term Memory (LSTM) networks, a specialized form of recurrent neural network (RNN) designed to handle time-series data and retain long-term dependencies. To provide a comparative benchmark, a Linear Regression model is also implemented. The proposed system preprocesses and normalizes historical stock price datasets, trains both models, and evaluates their performance using error metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Furthermore, an interactive Streamlit-based web interface is developed to facilitate user interaction, enabling the upload of datasets, visualization of past trends, and generation of future predictions in real-time. The results demonstrate the effectiveness of LSTM in outperforming traditional regression methods, highlighting its potential for real-world deployment in financial forecasting applications.