Smart Stock Price Prediction Algorithm using RNN variant Long Short-Term Memory

Stock price prediction plays a critical role in helping individuals and organizations make informed financial decisions. This research introduces an innovative model based on Long Short-Term Memory (LSTM), a type of Recurrent Neural Network (RNN), designed to forecast stock prices. The model leverages historical stock data, including key indicators such as opening and closing prices, daily highs and lows, and trading volumes. To ensure reliable predictions, the study incorporates a comprehensive preprocessing pipeline. This pipeline handles data cleaning and normalization to prepare the input data for analysis. The core of the model is built on advanced RNN architectures like LSTM and Gated Recurrent Units (GRUs), which are well-suited for capturing complex temporal patterns in sequential data—an essential aspect of accurate stock price forecasting. The model's performance is evaluated using standard error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). These metrics provide a clear measure of the model’s accuracy and reliability. The study highlights the power of RNNs in stock price prediction and introduces an interactive, user-friendly tool tailored for investors and traders, enabling them to refine their financial strategies and make better decisions.