Estimation of Market Price through LSTM Model

This project addresses the intricate challenge of accurate stock price prediction in financial markets. Recognizing the limitations of traditional methods in capturing the dynamic nature of stock prices, we utilize LSTM models, which are a kind of recurrent neural network. Our methodology involves comprehensive steps, including collecting and preprocessing extensive datasets, training LSTM networks, and evaluating their performance in predicting future stock prices. The outcome showcases the promising capabilities of our LSTM model in capturing complex temporal dependencies and trends within historical stock data. By using stringent evaluation measures like Mean Squared Error and accuracy, the model proves its dependability in making accurate predictions. This research significantly contributes to the advancement of stock price prediction methodologies, highlighting the potential of LSTM models for informed decision-making in the financial market.