Predictive Analysis of Stock Market Trends: A Machine Learning Approach

The stock market has been the case of this research in which LSTM neural networks have been investigated to enhance the accuracy of such predictions. With the evolution of financial markets, there is an increasing need to develop better and more reliable forecasting strategies; this research looks like it will help contribute to the literature on possibilities involving the use of LSTM networks for improving projections. These results are to demonstrate the potential of using LSTM networks in the process of stock market prediction as there is an increasing focus on machine learning for various financial applications. 0.89 was its R-squared score and 0.84 was cross-validation it performed well on the training and testing data. We chose the LSTM algorithm, saved this model as a Hierarchical Data Formats (HDF) donated h5 file, and then deployed it through Streamlit. The front end of the app accepts stock Ticker and provides predicted price from the trained model.