Abstract

Nothing is rock-steady in the stock market, which isa very volatile market. Nevertheless, there are a variety of ways and approaches one may utilise to learn about this dynamic movement and be prepared for it as technology develops. The focus of this essay is on different methods for quickly identifying market trends. The suggested strategy is comprehensive because it includes pre-processing the stock market dataset, a range of feature engineering techniques, and the integration of a customised deep learning-based system for forecasting stock market price patterns. The best and most suggested method for prediction is the model with the least amount of error. In order to conduct this study, we used three distinct models and ran sentiment analysis on news articles mentioning the firm or the stock. The results of this classification have given investors additional information to help them make decisions about where to stake their money as well as clear and incisive insight into the market's irregular ups and downs.