Stock Market Price Prediction Using Deep Learning Techniques

This paper presents that Making accurate forecasts about the stock market is still a main concern for finance because old models can miss sudden shifts. An LSTM-based approach is presented in this study to improve trading choices by making precise evaluations of prices. The method relies on historical Yahoo stock data (Open, High, Low, and Close volumes) and pays close attention to preprocessing such as checking the data and scaling its features, to maintain quality. Relying on LSTMs to notice temporal connections, the model accurately achieves a score of 89.5%. The results are further confirmed by MSE and MAE. Showing how the model predicts prices against actual prices proves its effectiveness and gives useful insights to traders. These developments are made available through a cloud-deployable interface that unites complicated AI with regular use, allowing for immediate analysis of data. They confirm that LSTMs are helpful in financial time-series work and lay out what user-friendly tools should look like. The results indicate that the analysis can be used with cryptocurrencies or commodities and that it meets the sector’s need for clear explanations. Future studies might experiment with mixed frameworks (such as Transformer and LSTM) and metrics based on risk (including the Sharpe ratio). It makes a difference by ensuring that technology and useful results go together, enabling people to face shifting markets with the support of data.