Prediction of Stock Prices using LSTM Model and Comparison with ML Algorithms

Guessing stock rates for the future is one of the key components of financial deals market analysis, since they can provide information that acts like risk management and investing alternatives. Deep learning’s (DLs) strong processing capabilities and aptitude to understand the non-technical, non- linear interactions have been able to establish considerable progress in stock price forecast. The LSTM networks are good for the forecasting in the problems related to time series such as stock price forecast since they have the capability of taking into account lagged effects and avoiding prospects like gradients that vanish. After considering the above performance matrix, which are the following: accuracy, precision, recall, and the F1-score, it will be possible for the models to be evaluated completely. These criteria give a comprehensive evaluation of the predictive accuracy, the robustness, and the generalization ability of the algorithms. Mind the results to show you that traditional ML methods, which can provide prompt and simple interpretable results, the LSTM-based model makes a better job at modeling the complex temporal patterns found in stock price data. The relative research sets out how DL can raise stock price prediction accuracy thus it enriches the financial analysts and investors with new insights. Our suggested LSTM model delivers 15.78% more accurate results than Linear Regression and 6.91% more accurate results than Random Forest, thus, it convinces everyone about its highlevel performance in stock price prediction.