Speculating Future Stock Price in Stock Market Using Feature Scaling and Predictive Models

Forecasting stock prices accurately is challenging due to market volatility. This project explores utilizing Long Short-Term Memory (LSTM) neural networks with Google Trends data to enhance prediction accuracy. LSTM is ideal for capturing complex patterns in financial data. We collect and preprocess historical stock data and relevant Google Trends search volumes to create a dataset for the model. The LSTM model learns patterns and correlations between Google Trends and stock market movements. aiming to capture valuable views and insights. We conduct experiments to compare the LSTM model's performance with traditional methods, evaluating metrics like accuracy, precision, and recall. The results demonstrate the potential of integrating Google Trends data with LSTM for more accurate stock market predictions, benefiting investors and analysts.